{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Dev\\tools\\anaconda\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n", "Using TensorFlow backend.\n" ] } ], "source": [ "from music21 import instrument, converter, stream, note, chord\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import keras\n", "import keras.layers as layers\n", "from keras import backend as K\n", "import os\n", "import pickle\n", "from itertools import compress" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "os.environ[\"CUDE_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n", "os.environ[\"CUDE_VISIBLE_DEVICES\"] = \"\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class Note:\n", " def __init__(self, pitch, octave, duration, offset):\n", " self.pitch = pitch\n", " self.octave = octave\n", " self.duration = int(duration * 24)\n", " self.offset = int(offset * 24)\n", " \n", "def make_note(n):\n", " n_conv = note.Note(n.pitch + str(n.octave))\n", " n_conv.quarterLength = n.duration / 24\n", " n_conv.offset = n.offset / 24\n", " n_conv.storedInstrument = instrument.Piano()\n", " return n_conv\n", "\n", "def write_to_stream(notes): \n", " notes.sort(key = lambda n: n.offset)\n", " notes_conv = [make_note(n) for n in notes]\n", " return stream.Stream(notes_conv)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Preprocessing for Autoencoder" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def convert_single(n):\n", " if type(n) is note.Note:\n", " return [Note(n.pitch.name, n.pitch.octave, n.quarterLength, n.offset)]\n", " if type(n) is chord.Chord:\n", " return [Note(p.name, p.octave, n.quarterLength, n.offset) for p in n.pitches]\n", " else:\n", " return []\n", "\n", "pitch_map = { \"C-\": -1, \"C\": 0, \"C#\": 1, \n", " \"D-\": 1, \"D\": 2, \"D#\": 3,\n", " \"E-\": 3, \"E\": 4, \"E#\": 5,\n", " \"F-\": 4, \"F\": 5, \"F#\": 6,\n", " \"G-\": 6, \"G\": 7, \"G#\": 8,\n", " \"A-\": 8, \"A\": 9, \"A#\": 10,\n", " \"B-\": 10, \"B\": 11,\"B#\": 12 }\n", "\n", "inv_pitch_map = { value: key for key, value in pitch_map.items() }\n", " \n", "def to_numerical_value(pitch, octave):\n", " return octave * 12 + pitch_map[pitch]\n", " \n", "def from_numerical_value(value):\n", " pitch = inv_pitch_map[value % 12]\n", " octave = value // 12\n", " return (pitch, octave)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def make_matrix(notes, size = 96):\n", " img = np.zeros((1, size, size))\n", " \n", " def add_to_matrix(mat, level, val, cols, carry = False):\n", " if cols: \n", " mat[level, val, cols] = 1.0\n", " return mat\n", "\n", " \n", " def add_level(mat):\n", " mat_concat = np.zeros((1, size, size))\n", " mat = np.concatenate((mat, mat_concat), axis = 0)\n", " return mat\n", " \n", "\n", " def cut_range(offset, duration, level, cut_off = False):\n", " if cut_off:\n", " return range(offset - level * 96, 96)\n", " return range(offset - level * 96, offset + duration - level * 96 - 1)\n", " \n", " \n", " def handle_note(n, mat):\n", " current_depth = mat.shape[0] - 1\n", " if n.offset + n.duration - current_depth * 96 > 96:\n", " mat = add_level(mat)\n", " return handle_note(n, mat)\n", " else:\n", " level = n.offset // 96\n", " val = to_numerical_value(n.pitch, n.octave)\n", " if level < (n.offset + n.duration) // 96:\n", " cols = cut_range(n.offset, n.duration, level, True)\n", " mat = add_to_matrix(mat, level, val, cols, True)\n", " remaining_duration = (n.duration - ((level + 1) * 96 - n.offset)) / 24\n", " if remaining_duration > 0:\n", " new_n = Note(n.pitch, n.octave, remaining_duration, (level + 1) * 4)\n", " return handle_note(new_n, mat)\n", " else:\n", " return mat\n", " else:\n", " cols = cut_range(n.offset, n.duration, level)\n", " if n.duration > 0:\n", " mat = add_to_matrix(mat, level, val, cols) \n", " return mat\n", " \n", " \n", " for n in notes:\n", " if type(n) is Note:\n", " img = handle_note(n, img)\n", " \n", " return img" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# LSTM Preprocessing" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def to_notewise(notes):\n", " notes = list(filter(lambda n: n.duration > 0, notes))\n", " notes.sort(key = lambda n: n.offset)\n", " notewise = []\n", " \n", " last_offset = 0\n", " if notes[0].offset > 0:\n", " wait_period = notes[0].offset\n", " while wait_period > 95:\n", " notewise.append(\"w95\")\n", " wait_period -= 95\n", " notewise.append(\"w\" + str(wait_period))\n", " last_offset = notes[0].offset\n", " \n", " note_stack = {}\n", " min_stop_offset = -1\n", " \n", " for n in notes:\n", " if last_offset < n.offset:\n", " while note_stack and min_stop_offset > 0 and min_stop_offset < n.offset:\n", " if min_stop_offset - last_offset > 0:\n", " wait_period = min_stop_offset - last_offset\n", " while wait_period > 95:\n", " notewise.append(\"w95\")\n", " wait_period -= 95\n", " notewise.append(\"w\" + str(wait_period))\n", " for n_id in note_stack[min_stop_offset]:\n", " notewise.append(\"e\" + str(n_id))\n", " del note_stack[min_stop_offset]\n", " last_offset = min_stop_offset\n", " if len(note_stack) > 0:\n", " min_stop_offset = min(note_stack.keys())\n", " else:\n", " min_stop_offset = -1\n", " \n", " note_id = to_numerical_value(n.pitch, n.octave)\n", " if last_offset < n.offset:\n", " wait_period = n.offset - last_offset\n", " while wait_period > 95:\n", " notewise.append(\"w95\")\n", " wait_period -= 95\n", " notewise.append(\"w\" + str(wait_period))\n", " last_offset = n.offset\n", " notewise.append(\"s\" + str(note_id))\n", "\n", " stop_time = n.offset + n.duration\n", " if stop_time in note_stack:\n", " note_stack[stop_time] += [note_id]\n", " else:\n", " note_stack[stop_time] = [note_id]\n", " if min_stop_offset < 0 or stop_time < min_stop_offset:\n", " min_stop_offset = stop_time\n", " return notewise\n", "\n", "def from_notewise(notewise):\n", " offset = 0\n", " notes = []\n", " note_stack = {}\n", " \n", " for n in notewise:\n", " n_id = int(n[1:len(n)])\n", " if n.startswith(\"w\"):\n", " offset += n_id\n", " elif n.startswith(\"s\"):\n", " note_stack[n_id] = offset\n", " else:\n", " if n_id in note_stack:\n", " pitch, octave = from_numerical_value(n_id)\n", " notes.append(Note(pitch, octave, (offset - note_stack[n_id]) / 24, note_stack[n_id] / 24))\n", " return notes " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def create_embedding():\n", " vocab_length = 96 * 3\n", " embed = {}\n", " symbols = [\"s\", \"e\", \"w\"]\n", " for k in range(len(symbols)):\n", " for i in range(96):\n", " vec = np.zeros(vocab_length)\n", " vec[k * 96 + i] = 1\n", " embed[symbols[k] + str(i)] = vec\n", " return embed\n", "\n", "lstm_embed = create_embedding()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Autoencoder Setup" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "shape = (10, 96, 96)\n", "momentum = 0.9\n", "length = 10" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [], "source": [ "def plot_history(history):\n", " \"\"\"Create a plot showing the training history of `model.fit`.\n", " \n", " Example:\n", " history = model.fit(...)\n", " plot_history(history)\n", " \"\"\"\n", " x = range(history.params['epochs'])\n", " acc, val_acc = history.history['acc'], history.history.get('val_acc')\n", " f, axarr = plt.subplots(2, sharex=True)\n", " axarr[0].set_title('accuracy')\n", " axarr[0].plot(x, acc, label='train')\n", " if val_acc:\n", " axarr[0].plot(x, val_acc, label='validation')\n", " axarr[0].legend()\n", " \n", " loss, val_loss = history.history['loss'], history.history.get('val_loss')\n", " axarr[1].set_title('loss')\n", " axarr[1].plot(x, loss, label='train')\n", " if val_loss:\n", " axarr[1].plot(x, val_loss, label='validation')\n", " axarr[1].legend()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def create_model(shape, momentum, length):\n", " model_input = keras.Input(shape = shape)\n", " layer_stack = layers.Reshape((shape[0], -1))(model_input)\n", " layer_stack = layers.TimeDistributed(layers.Dense(2048, activation = \"relu\"))(layer_stack)\n", " layer_stack = layers.TimeDistributed(layers.Dense(1024, activation = \"relu\"))(layer_stack)\n", " layer_stack = layers.Flatten()(layer_stack)\n", " layer_stack = layers.Dense(2048, activation = \"relu\")(layer_stack)\n", " layer_stack = layers.Dense(512, activation = \"relu\")(layer_stack)\n", " layer_stack = layers.BatchNormalization(momentum = momentum, name = \"encoder\")(layer_stack)\n", " layer_stack = layers.Dropout(0.2)(layer_stack)\n", " \n", " layer_stack = layers.Dense(2048, name=\"composer\")(layer_stack)\n", " layer_stack = layers.BatchNormalization(momentum=momentum)(layer_stack)\n", " layer_stack = layers.Activation(\"relu\")(layer_stack)\n", " layer_stack = layers.Dropout(0.2)(layer_stack)\n", "\n", " layer_stack = layers.Dense(length * 256)(layer_stack)\n", " layer_stack = layers.Reshape((length, 256))(layer_stack)\n", " layer_stack = layers.TimeDistributed(layers.BatchNormalization(momentum=momentum))(layer_stack)\n", " layer_stack = layers.Activation(\"relu\")(layer_stack)\n", " layer_stack = layers.Dropout(0.2)(layer_stack)\n", "\n", " layer_stack = layers.TimeDistributed(layers.Dense(2048))(layer_stack)\n", " layer_stack = layers.TimeDistributed(layers.BatchNormalization(momentum=momentum))(layer_stack)\n", " layer_stack = layers.Activation(\"relu\")(layer_stack)\n", " layer_stack = layers.Dropout(0.2)(layer_stack)\n", "\n", " layer_stack = layers.TimeDistributed(layers.Dense(shape[1] * shape[2], activation=\"sigmoid\"))(layer_stack)\n", " model_output = layers.Reshape(shape)(layer_stack)\n", " \n", " model = keras.Model(model_input, model_output)\n", " model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics = [\"acc\"])\n", " model.summary()\n", " return model" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def create_model(shape, momentum, length):\n", " model_input = keras.Input(shape = shape)\n", " layer_stack = layers.Reshape((shape[0], -1))(model_input)\n", " layer_stack = layers.TimeDistributed(layers.Dense(1024, activation = \"relu\"))(layer_stack)\n", " layer_stack = layers.TimeDistributed(layers.Dense(256, activation = \"relu\"))(layer_stack)\n", " layer_stack = layers.Flatten()(layer_stack)\n", " layer_stack = layers.Dense(1024, activation = \"relu\")(layer_stack)\n", " layer_stack = layers.Dense(128, activation = \"relu\")(layer_stack)\n", " layer_stack = layers.BatchNormalization(momentum = momentum, name = \"encoder\")(layer_stack)\n", " layer_stack = layers.Activation(\"relu\")(layer_stack)\n", " layer_stack = layers.Dropout(0.3)(layer_stack)\n", " \n", " layer_stack = layers.Dense(1024, name=\"composer\")(layer_stack)\n", " layer_stack = layers.BatchNormalization(momentum=momentum)(layer_stack)\n", " layer_stack = layers.Activation(\"relu\")(layer_stack)\n", " layer_stack = layers.Dropout(0.3)(layer_stack)\n", "\n", " layer_stack = layers.Dense(length * 256)(layer_stack)\n", " layer_stack = layers.Reshape((length, 256))(layer_stack)\n", " layer_stack = layers.TimeDistributed(layers.BatchNormalization(momentum=momentum))(layer_stack)\n", " layer_stack = layers.Activation(\"relu\")(layer_stack)\n", " layer_stack = layers.Dropout(0.3)(layer_stack)\n", "\n", " layer_stack = layers.TimeDistributed(layers.Dense(1024, activation = \"relu\"))(layer_stack)\n", " layer_stack = layers.TimeDistributed(layers.BatchNormalization(momentum=momentum))(layer_stack)\n", " layer_stack = layers.Activation(\"relu\")(layer_stack)\n", " layer_stack = layers.Dropout(0.3)(layer_stack)\n", "\n", " layer_stack = layers.TimeDistributed(layers.Dense(shape[1] * shape[2], activation=\"sigmoid\"))(layer_stack)\n", " model_output = layers.Reshape(shape)(layer_stack)\n", " \n", " model = keras.Model(model_input, model_output)\n", " model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics = [\"acc\"])\n", " model.summary()\n", " return model" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def create_composer(model):\n", " return K.function([model.get_layer(\"composer\").input, K.learning_phase()], [model.layers[-1].output])\n", "\n", "def create_encoder(model):\n", " return K.function([model.input, K.learning_phase()], [model.get_layer(\"encoder\").output])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# LSTM Setup" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def create_embedding():\n", " vocab_length = 96 * 3\n", " embed = {}\n", " symbols = [\"s\", \"e\", \"w\"]\n", " for k in range(len(symbols)):\n", " for i in range(96):\n", " vec = np.zeros(vocab_length)\n", " vec[k * 96 + i] = 1\n", " embed[symbols[k] + str(i)] = vec\n", " return embed\n", "\n", "def reverse_embedding(vec):\n", " for key, value in lstm_embed.items():\n", " if np.argmax(value) == np.argmax(vec):\n", " return key\n", "\n", "lstm_embed = create_embedding()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "lstm_vocab_length = 96 * 3\n", "lstm_memory_length = 30\n", "\n", "def create_lstm(mem_length = lstm_memory_length, voc_length = lstm_vocab_length):\n", " model = keras.Sequential()\n", " model.add(layers.LSTM(500, return_sequences = True, input_shape = (mem_length, voc_length)))\n", " model.add(layers.Dropout(0.2))\n", " model.add(layers.LSTM(400, return_sequences = True, input_shape = (mem_length, voc_length)))\n", " model.add(layers.Dropout(0.2))\n", " model.add(layers.LSTM(300, return_sequences = False))\n", " model.add(layers.Dropout(0.2))\n", " model.add(layers.Dense(voc_length, activation = \"softmax\"))\n", "\n", " model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])\n", " model.summary()\n", " return model" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def embed_song(notewise):\n", " embedded = []\n", " for n in notewise:\n", " embedded.append(lstm_embed[n])\n", " return np.array(embedded)\n", "\n", "def inverse_embed(embedded):\n", " notes = []\n", " for i in range(len(embedded)):\n", " notes.append(reverse_embedding(embedded[i]))\n", " return notes" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def make_lstm_training(song, mem_length = lstm_memory_length, step = 1):\n", " X = np.reshape(song[0:mem_length], (1, mem_length, lstm_vocab_length))\n", " y = np.reshape(song[mem_length], (1, lstm_vocab_length))\n", " for k in range(step, len(song) - mem_length, step):\n", " X = np.concatenate((X, np.reshape(song[k:(k + mem_length)], (1, mem_length, lstm_vocab_length))), axis = 0)\n", " y = np.concatenate((y, np.reshape(song[k + mem_length], (1, lstm_vocab_length))), axis = 0)\n", " return X, y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training Data Setup" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def load_midis(path):\n", " return [converter.parse(path + fname) for fname in os.listdir(path)]\n", "\n", "def save_object(obj, name):\n", " with open(name, \"wb\") as output:\n", " pickle.dump(obj, output, pickle.HIGHEST_PROTOCOL)\n", " \n", "def load_object(name):\n", " with open(name, \"rb\") as input:\n", " return pickle.load(input)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def convert_midis_to_notes(midis):\n", " return [[n for ch in [convert_single(n) if type(n) is not note.Rest else [] for n in m.flat.notesAndRests] for n in ch] for m in midis]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def convert_notes_to_images(images, sparse = False):\n", " return [make_matrix(song) for song in images] if not sparse else [make_sparse_matrix(song) for song in images]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": true }, "outputs": [], "source": [ "def create_training_set(tensors, shape, num_measures = 10):\n", " pre = [[img[k*10: (k+1)*10, :, :] for k in range(0, np.shape(img)[0] // num_measures)] for img in tensors]\n", " pre_flat = [img for img_list in [tensors for tensors in pre] for img in img_list]\n", " \n", " train = pre_flat[0].reshape((1, *shape)) \n", " for i in range(1, len(pre_flat)):\n", " train = np.concatenate((train, pre_flat[i].reshape((1, *shape))), axis = 0)\n", " return train" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "composer = \"beethoven\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#midis = load_midis(\"data/train_\" + composer + \"/\")\n", "#save_object(midis, \"midis_\" + composer + \".pkl\")\n", "midis = load_object(\"midis_\" + composer + \".pkl\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "midis = compress(midis, [len(m) < 3 for m in midis])" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [], "source": [ "#note_collection = convert_midis_to_notes(midis)\n", "#save_object(note_collection, \"notes_\" + composer + \".pkl\")\n", "note_collection = load_object(\"notes_\" + composer + \".pkl\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#image_collection = convert_notes_to_images(note_collection)\n", "#save_object(image_collection, \"images_\" + composer + \".pkl\")\n", "#image_collection_sparse = convert_notes_to_images(note_collection, sparse = True)\n", "#save_object(image_collection_sparse, \"image_sparse_chopin.pkl\")\n", "image_collection = load_object(\"images_\" + composer + \".pkl\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "#training_set = create_training_set(image_collection, shape)\n", "#save_object(training_set, \"train_\" + composer + \".pkl\")\n", "training_set = load_object(\"train_\" + composer + \".pkl\")" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_8 (InputLayer) (None, 10, 96, 96) 0 \n", "_________________________________________________________________\n", "reshape_22 (Reshape) (None, 10, 9216) 0 \n", "_________________________________________________________________\n", "time_distributed_56 (TimeDis (None, 10, 1024) 9438208 \n", "_________________________________________________________________\n", "time_distributed_57 (TimeDis (None, 10, 256) 262400 \n", "_________________________________________________________________\n", "flatten_8 (Flatten) (None, 2560) 0 \n", "_________________________________________________________________\n", "dense_65 (Dense) (None, 1024) 2622464 \n", "_________________________________________________________________\n", "dense_66 (Dense) (None, 128) 131200 \n", "_________________________________________________________________\n", "encoder (BatchNormalization) (None, 128) 512 \n", "_________________________________________________________________\n", "activation_22 (Activation) (None, 128) 0 \n", "_________________________________________________________________\n", "dropout_29 (Dropout) (None, 128) 0 \n", "_________________________________________________________________\n", "composer (Dense) (None, 1024) 132096 \n", "_________________________________________________________________\n", "batch_normalization_22 (Batc (None, 1024) 4096 \n", "_________________________________________________________________\n", "activation_23 (Activation) (None, 1024) 0 \n", "_________________________________________________________________\n", "dropout_30 (Dropout) (None, 1024) 0 \n", "_________________________________________________________________\n", "dense_67 (Dense) (None, 2560) 2624000 \n", "_________________________________________________________________\n", "reshape_23 (Reshape) (None, 10, 256) 0 \n", "_________________________________________________________________\n", "time_distributed_58 (TimeDis (None, 10, 256) 1024 \n", "_________________________________________________________________\n", "activation_24 (Activation) (None, 10, 256) 0 \n", "_________________________________________________________________\n", "dropout_31 (Dropout) (None, 10, 256) 0 \n", "_________________________________________________________________\n", "time_distributed_59 (TimeDis (None, 10, 1024) 263168 \n", "_________________________________________________________________\n", "time_distributed_60 (TimeDis (None, 10, 1024) 4096 \n", "_________________________________________________________________\n", "activation_25 (Activation) (None, 10, 1024) 0 \n", "_________________________________________________________________\n", "dropout_32 (Dropout) (None, 10, 1024) 0 \n", "_________________________________________________________________\n", "time_distributed_61 (TimeDis (None, 10, 9216) 9446400 \n", "_________________________________________________________________\n", "reshape_24 (Reshape) (None, 10, 96, 96) 0 \n", "=================================================================\n", "Total params: 24,929,664\n", "Trainable params: 24,924,800\n", "Non-trainable params: 4,864\n", "_________________________________________________________________\n" ] } ], "source": [ "model = create_model(shape, momentum, length)" ] }, { "cell_type": "code", "execution_count": 160, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 160 samples, validate on 40 samples\n", "Epoch 1/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0281 - acc: 0.9896 - val_loss: 0.0785 - val_acc: 0.9815\n", "Epoch 2/100\n", "160/160 [==============================] - 5s 32ms/step - loss: 0.0284 - acc: 0.9895 - val_loss: 0.0771 - val_acc: 0.9821\n", "Epoch 3/100\n", "160/160 [==============================] - 5s 32ms/step - loss: 0.0282 - acc: 0.9895 - val_loss: 0.0767 - val_acc: 0.9822\n", "Epoch 4/100\n", "160/160 [==============================] - 5s 32ms/step - loss: 0.0273 - acc: 0.9898 - val_loss: 0.0775 - val_acc: 0.9818\n", "Epoch 5/100\n", "160/160 [==============================] - 5s 32ms/step - loss: 0.0273 - acc: 0.9898 - val_loss: 0.0783 - val_acc: 0.9817\n", "Epoch 6/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0274 - acc: 0.9898 - val_loss: 0.0790 - val_acc: 0.9820\n", "Epoch 7/100\n", "160/160 [==============================] - 5s 32ms/step - loss: 0.0265 - acc: 0.9901 - val_loss: 0.0789 - val_acc: 0.9818\n", "Epoch 8/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0279 - acc: 0.9897 - val_loss: 0.0795 - val_acc: 0.9819\n", "Epoch 9/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0277 - acc: 0.9897 - val_loss: 0.0799 - val_acc: 0.9814\n", "Epoch 10/100\n", "160/160 [==============================] - 5s 32ms/step - loss: 0.0266 - acc: 0.9901 - val_loss: 0.0786 - val_acc: 0.9817\n", "Epoch 11/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0286 - acc: 0.9895 - val_loss: 0.0793 - val_acc: 0.9812\n", "Epoch 12/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0271 - acc: 0.9901 - val_loss: 0.0797 - val_acc: 0.9812\n", "Epoch 13/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0285 - acc: 0.9896 - val_loss: 0.0777 - val_acc: 0.9820\n", "Epoch 14/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0282 - acc: 0.9896 - val_loss: 0.0777 - val_acc: 0.9820\n", "Epoch 15/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0273 - acc: 0.9898 - val_loss: 0.0792 - val_acc: 0.9815\n", "Epoch 16/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0280 - acc: 0.9897 - val_loss: 0.0790 - val_acc: 0.9819\n", "Epoch 17/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0264 - acc: 0.9901 - val_loss: 0.0783 - val_acc: 0.9822\n", "Epoch 18/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0277 - acc: 0.9897 - val_loss: 0.0785 - val_acc: 0.9821\n", "Epoch 19/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0271 - acc: 0.9899 - val_loss: 0.0774 - val_acc: 0.9823\n", "Epoch 20/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0269 - acc: 0.9901 - val_loss: 0.0800 - val_acc: 0.9819\n", "Epoch 21/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0265 - acc: 0.9901 - val_loss: 0.0802 - val_acc: 0.9816\n", "Epoch 22/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0264 - acc: 0.9901 - val_loss: 0.0782 - val_acc: 0.9819\n", "Epoch 23/100\n", "160/160 [==============================] - 5s 29ms/step - loss: 0.0269 - acc: 0.9901 - val_loss: 0.0760 - val_acc: 0.9824\n", "Epoch 24/100\n", "160/160 [==============================] - 5s 29ms/step - loss: 0.0257 - acc: 0.9905 - val_loss: 0.0763 - val_acc: 0.9824\n", "Epoch 25/100\n", "160/160 [==============================] - 4s 28ms/step - loss: 0.0266 - acc: 0.9901 - val_loss: 0.0768 - val_acc: 0.9822\n", "Epoch 26/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0258 - acc: 0.9904 - val_loss: 0.0767 - val_acc: 0.9823\n", "Epoch 27/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0281 - acc: 0.9896 - val_loss: 0.0762 - val_acc: 0.9825\n", "Epoch 28/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0262 - acc: 0.9901 - val_loss: 0.0764 - val_acc: 0.9820\n", "Epoch 29/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0268 - acc: 0.9900 - val_loss: 0.0771 - val_acc: 0.9820\n", "Epoch 30/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0263 - acc: 0.9901 - val_loss: 0.0773 - val_acc: 0.9822\n", "Epoch 31/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0257 - acc: 0.9904 - val_loss: 0.0768 - val_acc: 0.9827\n", "Epoch 32/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0263 - acc: 0.9903 - val_loss: 0.0774 - val_acc: 0.9822\n", "Epoch 33/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0271 - acc: 0.9899 - val_loss: 0.0774 - val_acc: 0.9822\n", "Epoch 34/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0272 - acc: 0.9899 - val_loss: 0.0786 - val_acc: 0.9818\n", "Epoch 35/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0263 - acc: 0.9901 - val_loss: 0.0797 - val_acc: 0.9816\n", "Epoch 36/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0273 - acc: 0.9901 - val_loss: 0.0776 - val_acc: 0.9823\n", "Epoch 37/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0262 - acc: 0.9904 - val_loss: 0.0766 - val_acc: 0.9825\n", "Epoch 38/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0267 - acc: 0.9900 - val_loss: 0.0760 - val_acc: 0.9825\n", "Epoch 39/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0254 - acc: 0.9906 - val_loss: 0.0767 - val_acc: 0.9823\n", "Epoch 40/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0246 - acc: 0.9909 - val_loss: 0.0779 - val_acc: 0.9821\n", "Epoch 41/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0250 - acc: 0.9907 - val_loss: 0.0779 - val_acc: 0.9821\n", "Epoch 42/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0270 - acc: 0.9901 - val_loss: 0.0782 - val_acc: 0.9817\n", "Epoch 43/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0263 - acc: 0.9902 - val_loss: 0.0782 - val_acc: 0.9821\n", "Epoch 44/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0258 - acc: 0.9904 - val_loss: 0.0786 - val_acc: 0.9823\n", "Epoch 45/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0247 - acc: 0.9909 - val_loss: 0.0794 - val_acc: 0.9821\n", "Epoch 46/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0257 - acc: 0.9904 - val_loss: 0.0778 - val_acc: 0.9823\n", "Epoch 47/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0243 - acc: 0.9909 - val_loss: 0.0791 - val_acc: 0.9818\n", "Epoch 48/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0249 - acc: 0.9907 - val_loss: 0.0800 - val_acc: 0.9812\n", "Epoch 49/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0256 - acc: 0.9906 - val_loss: 0.0795 - val_acc: 0.9811\n", "Epoch 50/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0257 - acc: 0.9905 - val_loss: 0.0782 - val_acc: 0.9818\n", "Epoch 51/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0262 - acc: 0.9903 - val_loss: 0.0777 - val_acc: 0.9822\n", "Epoch 52/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0258 - acc: 0.9905 - val_loss: 0.0788 - val_acc: 0.9820\n", "Epoch 53/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0264 - acc: 0.9902 - val_loss: 0.0781 - val_acc: 0.9822\n", "Epoch 54/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0251 - acc: 0.9907 - val_loss: 0.0777 - val_acc: 0.9822\n", "Epoch 55/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0269 - acc: 0.9901 - val_loss: 0.0781 - val_acc: 0.9822\n", "Epoch 56/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0280 - acc: 0.9898 - val_loss: 0.0773 - val_acc: 0.9824\n", "Epoch 57/100\n", "160/160 [==============================] - 4s 28ms/step - loss: 0.0244 - acc: 0.9910 - val_loss: 0.0765 - val_acc: 0.9824\n", "Epoch 58/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0255 - acc: 0.9905 - val_loss: 0.0770 - val_acc: 0.9822\n", "Epoch 59/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0254 - acc: 0.9906 - val_loss: 0.0775 - val_acc: 0.9819\n", "Epoch 60/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0251 - acc: 0.9907 - val_loss: 0.0781 - val_acc: 0.9819\n", "Epoch 61/100\n", "160/160 [==============================] - 5s 29ms/step - loss: 0.0246 - acc: 0.9908 - val_loss: 0.0797 - val_acc: 0.9813\n", "Epoch 62/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0249 - acc: 0.9908 - val_loss: 0.0789 - val_acc: 0.9817\n", "Epoch 63/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0262 - acc: 0.9902 - val_loss: 0.0791 - val_acc: 0.9817\n", "Epoch 64/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0265 - acc: 0.9903 - val_loss: 0.0758 - val_acc: 0.9823\n", "Epoch 65/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0293 - acc: 0.9893 - val_loss: 0.0911 - val_acc: 0.9801\n", "Epoch 66/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0338 - acc: 0.9882 - val_loss: 0.1173 - val_acc: 0.9761\n", "Epoch 67/100\n", "160/160 [==============================] - 4s 28ms/step - loss: 0.0320 - acc: 0.9886 - val_loss: 0.0897 - val_acc: 0.9812\n", "Epoch 68/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0335 - acc: 0.9881 - val_loss: 0.0828 - val_acc: 0.9805\n", "Epoch 69/100\n", "160/160 [==============================] - 4s 28ms/step - loss: 0.0344 - acc: 0.9879 - val_loss: 0.0820 - val_acc: 0.9803\n", "Epoch 70/100\n", "160/160 [==============================] - 5s 29ms/step - loss: 0.0330 - acc: 0.9883 - val_loss: 0.0786 - val_acc: 0.9824\n", "Epoch 71/100\n", "160/160 [==============================] - 4s 28ms/step - loss: 0.0332 - acc: 0.9882 - val_loss: 0.0872 - val_acc: 0.9790\n", "Epoch 72/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0304 - acc: 0.9890 - val_loss: 0.0833 - val_acc: 0.9813\n", "Epoch 73/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0317 - acc: 0.9886 - val_loss: 0.0805 - val_acc: 0.9818\n", "Epoch 74/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0298 - acc: 0.9891 - val_loss: 0.0827 - val_acc: 0.9810\n", "Epoch 75/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0307 - acc: 0.9888 - val_loss: 0.0804 - val_acc: 0.9814\n", "Epoch 76/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0310 - acc: 0.9888 - val_loss: 0.0832 - val_acc: 0.9805\n", "Epoch 77/100\n", "160/160 [==============================] - 5s 29ms/step - loss: 0.0323 - acc: 0.9886 - val_loss: 0.0877 - val_acc: 0.9801\n", "Epoch 78/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0318 - acc: 0.9889 - val_loss: 0.0841 - val_acc: 0.9814\n", "Epoch 79/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0288 - acc: 0.9895 - val_loss: 0.0788 - val_acc: 0.9816\n", "Epoch 80/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0296 - acc: 0.9896 - val_loss: 0.0797 - val_acc: 0.9814\n", "Epoch 81/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0312 - acc: 0.9890 - val_loss: 0.0848 - val_acc: 0.9797\n", "Epoch 82/100\n", "160/160 [==============================] - 5s 29ms/step - loss: 0.0299 - acc: 0.9892 - val_loss: 0.0901 - val_acc: 0.9788\n", "Epoch 83/100\n", "160/160 [==============================] - 5s 31ms/step - loss: 0.0296 - acc: 0.9893 - val_loss: 0.0835 - val_acc: 0.9807\n", "Epoch 84/100\n", "160/160 [==============================] - 5s 29ms/step - loss: 0.0289 - acc: 0.9897 - val_loss: 0.0804 - val_acc: 0.9816\n", "Epoch 85/100\n", "160/160 [==============================] - 5s 30ms/step - loss: 0.0299 - acc: 0.9890 - val_loss: 0.0803 - val_acc: 0.9817\n", "Epoch 86/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0281 - acc: 0.9898 - val_loss: 0.0787 - val_acc: 0.9824\n", "Epoch 87/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0276 - acc: 0.9899 - val_loss: 0.0798 - val_acc: 0.9823\n", "Epoch 88/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0284 - acc: 0.9897 - val_loss: 0.0826 - val_acc: 0.9820\n", "Epoch 89/100\n", "160/160 [==============================] - 4s 28ms/step - loss: 0.0299 - acc: 0.9893 - val_loss: 0.0827 - val_acc: 0.9813\n", "Epoch 90/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0301 - acc: 0.9892 - val_loss: 0.0791 - val_acc: 0.9814\n", "Epoch 91/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0289 - acc: 0.9896 - val_loss: 0.0769 - val_acc: 0.9817\n", "Epoch 92/100\n", "160/160 [==============================] - 4s 28ms/step - loss: 0.0281 - acc: 0.9898 - val_loss: 0.0796 - val_acc: 0.9811\n", "Epoch 93/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0290 - acc: 0.9896 - val_loss: 0.0877 - val_acc: 0.9778\n", "Epoch 94/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0277 - acc: 0.9899 - val_loss: 0.0853 - val_acc: 0.9791\n", "Epoch 95/100\n", "160/160 [==============================] - 4s 28ms/step - loss: 0.0287 - acc: 0.9895 - val_loss: 0.0805 - val_acc: 0.9812\n", "Epoch 96/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0261 - acc: 0.9903 - val_loss: 0.0813 - val_acc: 0.9818\n", "Epoch 97/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0270 - acc: 0.9900 - val_loss: 0.0818 - val_acc: 0.9816\n", "Epoch 98/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0273 - acc: 0.9900 - val_loss: 0.0821 - val_acc: 0.9816\n", "Epoch 99/100\n", "160/160 [==============================] - 4s 28ms/step - loss: 0.0258 - acc: 0.9905 - val_loss: 0.0815 - val_acc: 0.9813\n", "Epoch 100/100\n", "160/160 [==============================] - 5s 28ms/step - loss: 0.0271 - acc: 0.9903 - val_loss: 0.0783 - val_acc: 0.9814\n" ] } ], "source": [ "history = model.fit(training_set[0:200], training_set[0:200], validation_split = 0.2, batch_size = 20, epochs = 100, verbose = True)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "composers = [\"bach\", \"beethoven\", \"chopin\", \"grieg\", \"mozart\"]\n", "training_sets_full = {}\n", "\n", "for composer in composers:\n", " training_sets_full[composer] = load_object(\"train_\" + composer + \".pkl\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "min_training_length = np.min([len(training_sets_full[composer]) for composer in composers])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "training_sets = {}\n", "\n", "for composer in composers:\n", " training_sets[composer] = training_sets_full[composer][0:min_training_length]\n", "\n", "del training_sets_full" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "epochs = 10\n", "add_epochs = [15, 25, 50]\n", "auto_nn = {}\n", "\n", "def train_composer(composer, training_sets, epochs):\n", " model = create_model(shape, momentum, length)\n", " history = model.fit(training_sets[composer], training_sets[composer], \n", " validation_split = 0.15, batch_size = 10, epochs = epochs, verbose = True)\n", " return model, (history.history[\"acc\"], history.history[\"loss\"], history.history[\"val_acc\"], history.history[\"val_loss\"])\n", "\n", "def resume_training_composer(composer, nn, training_sets, epochs):\n", " history = nn.fit(training_sets[composer], training_sets[composer],\n", " validation_split = 0.15, batch_size = 10, epochs = epochs)\n", " return (history.history[\"acc\"], history.history[\"loss\"], history.history[\"val_acc\"], history.history[\"val_loss\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for composer in composers:\n", " print(\"Training \" + composer + \"...\")\n", " auto_nn[composer] = train_composer(composer, training_sets, epochs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for composer in composers:\n", " auto_nn[composer][0].save(\"auto_nn_\" + str(epochs) + \"_\" + composer + \".h5\")\n", " save_object(auto_nn[composer][1], \"history_auto_nn_\" + str(epochs) + \"_\" + composer + \".pkl\")\n", "del auto_nn" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for composer in composers:\n", " print(\"Training \" + composer + \"...\")\n", " auto_nn = keras.models.load_model(\"auto_nn_10_\" + composer + \".h5\")\n", " added = 0\n", " for e in add_epochs:\n", " added += e\n", " history = resume_training_composer(composer, auto_nn, training_sets, e)\n", " auto_nn.save(\"auto_nn_\" + str(epochs + added) + \"_\" + composer + \".h5\")\n", " save_object(history, \"history_auto_nn_\" + str(epochs + added) + \"_\" + composer + \".pkl\")\n", " del history\n", " del auto_nn" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "auto_composers = {}\n", "for composer in composers:\n", " auto_composers[composer] = (create_encoder(auto_nn[composer][0]), create_composer(auto_nn[composer][0]))" ] }, { "cell_type": "code", "execution_count": 184, "metadata": {}, "outputs": [], "source": [ "def plot_history(composer, epochs):\n", " def concat(x, y):\n", " return np.concatenate((np.reshape(x, (len(x), 1)), np.reshape(y, (len(y), 1))), axis = 1)\n", " def plot(data, composer, label):\n", " plt.plot([k for k in range(1, 101)], data, \"-\")\n", " plt.title(\"Autoencoder for \" + composer + \": \" + label)\n", " plt.xlabel(\"Epochs\")\n", " plt.ylabel(label)\n", " plt.savefig(\"auto_encoder_\" + composer + \"_\" + label + \"_plot.png\")\n", " plt.show()\n", " \n", " histories = []\n", " for e in epochs:\n", " histories.append(load_object(\"history_auto_nn_\" + str(e) + \"_\" + composer + \".pkl\"))\n", " acc = concat(histories[0][0], histories[0][2])\n", " loss = concat(histories[0][1], histories[0][3])\n", " for i in range(1, len(histories)):\n", " acc = np.concatenate((acc, concat(histories[i][0], histories[i][2])), axis = 0)\n", " loss = np.concatenate((loss, concat(histories[i][1], histories[i][3])), axis = 0)\n", " plot(acc, composer, \"Accuracy\")\n", " plot(loss, composer, \"Loss\")\n", " \n", "def plot_beethoven(epochs):\n", " def concat(x, y):\n", " return np.concatenate((np.reshape(x, (len(x), 1)), np.reshape(y, (len(y), 1))), axis = 1)\n", " def plot(data, label):\n", " plt.plot([k for k in range(1, 101)], data, \"-\")\n", " plt.title(\"Autoencoder for full Beethoven set: \" + label)\n", " plt.xlabel(\"Epochs\")\n", " plt.ylabel(label)\n", " plt.savefig(\"beethoven_full_\" + label + \"_plot.png\")\n", " plt.show()\n", " \n", " histories = []\n", " for e in epochs:\n", " histories.append(load_object(\"history_beethoven_full_\" + str(e) + \".pkl\"))\n", " acc = concat(histories[0][0], histories[0][2])\n", " loss = concat(histories[0][1], histories[0][3])\n", " for i in range(1, len(histories)):\n", " acc = np.concatenate((acc, concat(histories[i][0], histories[i][2])), axis = 0)\n", " loss = np.concatenate((loss, concat(histories[i][1], histories[i][3])), axis = 0)\n", " plot(acc, composer, \"Accuracy\")\n", " plot(loss, composer, \"Loss\")" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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SsMIRMTYidomI01h6iOah8kPyiFMrAJdExHZF3UYV75tzi+P+mlK6vM41lomU0tMppS8Uy2Ben5aeU0R8iTws7yzgPcXvbyCXFuuPR8QRpRvliNggIn5BnqCz3ghzzbqrWG8VETvWOygiJlbkl41vo7xVI+KVDZZVoa3P1SrA/cXfm61LX7oUn6m9KAebf6k459sRcX5E7B8Ra1Q857WLQQA2ophcso3nLQ2/diYRcXFxeXkv5K4fpUm9XtvkOZdQY6Iz8kzfqWJ5jvLkX1+nzkSExbm/qDjvefKEb9OAgyqOGVV13CLK/ehTUdZRdeo8ipwwWlm/mUUdl1Rsu6LqvIkMMLEaObhKwKQa+1areN6lOs6oeF2Wej3II0xNrti/oKhn6fE8qmZPrzh3K8oTIyby6F2lidTuAz7X6PmQg7fK12N2jfo+VHXO7gwwkWST76sBr0PuRz+D/r/DuRWPb6eYlbvqvEkMMFFmk3WcRsVEhE2eU3ciwlafU8V7ah55bot6y+8qzhlN7mpX+fmpfF99jfqThU5s9L6pOvbKimtOp/xZ3qnG9dqdiHCgZUrFeYP+XJE/v6nqnOnFa1fa9gCwXsU5p1Sd80LxO63c9pV23ocuLiNhsQVEUjtK3a/uTSnd1fDIsguK9X6lb/QBUkqnkkfTuoF809tD/rb831JK3xzgmkcC/0Xuez2GnCy6IXno3NL1F6eUDiNP3jaZHKiMI3+DfA55Bu8f1bp4ce5R5FaSX5MnWxxN/ub5EXJy6GHA/rXOb1XK3db2LK79V/KNZqnOV5JHyflD1TlzgH2Aj5CHPp5Dvnl6mDyJ2tYppYvqlPdPch7Pz4oy+sg3oicD2xflN6rvf5K79fyEHLAEeUjWf5Fbcz4B1P1me1lLeZLErcjP517y81sE3ETuQrNjSunp4apfK9p8TmNoPGzyS9/Ap5xz8jbgO+QJJZcU5VwK7JtS+hadcQA52H+I/F4vfZYbDVCxzLX4uZoJvJscVPydnJy+MjkwvxE4FnhjSqlyCN+TyV/GXET+fQb59/QouZVy15TSt5G6XKSUhrsOkiRJkl4mbAGRJEmSNGQMQCRJkiQNGQMQSZIkSUPGAESSJEnSkDEAkSRJkjRkDEAkSZIkDRkDEEmSJElDxgBEkiRJ0pAxAJEkSZI0ZAxAJEmSJA2Z3uGugNoTEQ8BqwDThrkqkiRJWr6NB2amlDZq5yIGIN1vlRVWWGGNLbfcco3hrogkSZKWX1OnTmXu3LltX8cApPtN23LLLde4+eabh7sekiRJWo5tu+223HLLLdPavY45IJIkSZKGjAGIJEmSpCFjACJJkiRpyBiASJIkSRoyXRmARMR6EfHziHgiIuZHxLSIOCUiVm/y/JUi4oMRcXZE3B0RsyPixYi4KSI+HxGjG5y7VUScFxFPR8S8iLgnIo6PiBUanPOWiPhTRMyIiDkRcUdEfDYiRrXy/CVJkqRu1XWjYEXEa4DrgLWAi4C7gR2AzwB7R8TOKaXpA1zmrcCvgRnAFcCFwBrAvsB/AwdExF4ppXlVZe8IXA70AecDjwJ7Al8H9irOmV91zn7ABcA84DdFmfsCJwM7Awe38DJIkiRJXanrAhDgR+Tg4+iU0g9KGyPi+8AxwAnAkQNc40ngQ8BvU0oLKq6xMjAFeAvwSeCkin2jgDOBFYH9Ukp/KLb3AOcBBxblf6finFWAnwKLgd1TSjcV279GDmQOioj3pZTOHfSrIEmSJHWhruqCFREbAxPIs36fVrX7G8Bs4NCIWKnRdVJKt6WUzqoMPortL1IOOnavOm03YEvgqlLwUZyzBPiP4uGREREV5xwErAmcWwo+inPmAV8tHn6iUV0lSZKk5UlXBSDk7k4Ak4sb/5cUwcO15BaKndooY2GxXlSn7EuqT0gpPQjcC2wIbNzMOcBVwBzgLRExpuXaSpIkSV2k2wKQzYv1vXX231esN2ujjCOKdXXQ0ErZdc9JKS0CHiJ3g9u4en+1iLi51gJsMdC5kiRJ0kjRbQHIqsX6hTr7S9tXa+XiEfEpYG/gNuDnHSh7mdZXkiRJ6jbdmITeSCn/Ig36xIgDgFPICeoHppQWDnBKJ8pu+pyU0rY1L5BbQbYZRJmSJEnSsOm2FpBSi8GqdfavUnVcUyJif+Bc4GnyaFUPdqjsZVJfSZIkqVt1WwByT7Gul+OxabGul6exlIg4GPgt8BSwW0rpnjqHtlJ23XMiohfYiJzsXivgkSRJkpY73RaAXFGsJxTzb7ykmMNjZ2AucEMzF4uIDwDnAE+Qg4/7Ghx+ebHeu8Z1NiYHGQ/TP5ioew6wK3nEruuqJy+UJEmSllddFYCklB4AJgPjyRMFVjoeWAn4ZUppdmljRGwREUuNFBURhwG/Ah4Bdq3T7arSlcBUYNeIeE/FdXqAE4uHZ6SUKvM5zgeeBd4XEdtVnDMW+M/i4ekDlDsipZRYtHgJ8xYuHu6qSJIkqYt0YxL6UcB1wKkRsRc5KNgR2IPc/enYquOnFuuXJgiMiD3Io1z1kFtVDu8/fyAAz6eUTik9SCktjojDya0a50fE+eTgZS9gO/IcJCdXXiClNDMiPkoORKZExLnADOA95CF6zwd+08JrMKxemLOQN35rMinBymN7ufO4dwx3lSRJktQlui4ASSk9ULQmfJPctemdwL+AU4HjU0ozmrjMhpRbf46oc8zD5FGxKsv+W0RsT25tmQCsXBz3TeA7tbpSpZQujIjdyIHRgcBY4H7gc8CpVS0mXaGvNyjVeuHiJY0PliRJkip0XQACkFJ6FDi8yWOXatpIKU0CJrVY9j+Bgwd5zrXkQGm50Deq3HNv4eKui58kSZI0jLoqB0QjQ29POaZbvCSxeIlBiCRJkppjAKJBiwhG91a2gtgNS5IkSc0xAFFLRo8yAJEkSdLgGYCoJX2jyt2wzAORJElSswxA1JI+W0AkSZLUAgMQtaQyAFmwyABEkiRJzTEAUUtMQpckSVIrDEDUEnNAJEmS1AoDELXEHBBJkiS1wgBELemXA2IAIkmSpCYZgKgl/eYBMQldkiRJTTIAUUv6es0BkSRJ0uAZgKgl5oBIkiSpFQYgakllADLfLliSJElqkgGIWjLaFhBJkiS1wABELek/D4gBiCRJkppjAKKWmAMiSZKkVhiAqCV9vZXzgDgKliRJkppjAKKWOA+IJEmSWmEAopaYAyJJkqRWGICoJeaASJIkqRUGIGpJZQBiDogkSZKaZQCilozutQVEkiRJg2cAopaYhC5JkqRWGICoJSahS5IkqRUGIGqJ84BIkiSpFQYgaomjYEmSJKkVBiBqyWgDEEmSJLXAAEQtsQVEkiRJrTAAUUsqk9AXLDIHRJIkSc0xAFFL+pwHRJIkSS0wAFFLzAGRJElSKwxA1BJzQCRJktQKAxC1pF8OiPOASJIkqUkGIGpJvxaQRbaASJIkqTkGIGrJ6H4zoRuASJIkqTkGIGqJOSCSJElqhQGIWlKZA2IXLEmSJDXLAEQtqRyG1yR0SZIkNcsARC2xC5YkSZJaYQCiljgTuiRJklphAKKW9MsBMQCRJElSkwxA1JK+nsoWkERK5oFIkiRpYAYgaklPT9DbU9kKYgAiSZKkgXVlABIR60XEzyPiiYiYHxHTIuKUiFh9ENd4e0ScFBGXRcSMiEgRcU2D448rjmm0PFB1zu4DHP+ddl6H4WYiuiRJkgard7grMFgR8RrgOmAt4CLgbmAH4DPA3hGxc0ppehOX+iSwHzAPuB8YKHiZ0mDfvsA2wJ/r7L+yzvl1A55uMLq3h7kLFwMGIJIkSWpO1wUgwI/IwcfRKaUflDZGxPeBY4ATgCObuM6JwLHkAGZ94KFGB6eUplAjiIiIUcC/Fw9/Uuf0KSml45qoU1fp6zcXiAGIJEmSBtZVXbAiYmNgAjANOK1q9zeA2cChEbHSQNdKKV2fUrorpbS4zWq9E1gPuCGldEeb1+oqo0eZAyJJkqTB6bYWkD2L9eSUUr+v3FNKL0bEteQAZSfgsiGq08eKdb3WD4BNIuJTwCrAk8DVKaX7BlNIRNxcZ9cWg7lOJ/WbC2SRLSCSJEkaWLcFIJsX63vr7L+PHIBsxhAEIBGxLrAP8ALwmwaHfrBYKs+9APhoSum5ZVfDZcskdEmSJA1WtwUgqxbrF+rsL21fbQjqAvARYBTw65TSnBr7nwG+BPwfudvYWGA74NvAgcA6EbFrdWtOLSmlbWttL1pGtmmp9m0yB0SSJEmD1W0ByEBKSQnLPCEhInqAI4qHNbtfpZTuAu6q2DQLuCQirgNuA3Ymj6B10TKs6jJjDogkSZIGq6uS0Cm3cKxaZ/8qVcctS/sAG9BC8nlKaSZwdvFw105XbKjYBUuSJEmD1W0ByD3FerM6+zct1vVyRDqplHz+4xbPf6ZYDzhi10jVLwAxCV2SJElN6LYA5IpiPaHoAvWSiFiZ3KVpLnDDsqxERLwaeBe5peW8Fi+zU7F+sCOVGgaVo2CZAyJJkqRmdFUAklJ6AJgMjCfPZF7peHJrwi9TSrNLGyNii4jo9FC1/05OPv9VneTzUtk7VwdKxfYPAYcAC2g9gBl25oBIkiRpsLoxCf0o4Drg1IjYC5gK7AjsQe56dWzV8VOLdVRujIhdyKNYAYwr1ptGxKTSMSmlidWFFwHFQDOfl5wF9BRJ54+RR8HaHtgBWAR8PKU0bYBrjFjmgEiSJGmwui4ASSk9EBHbAd8E9ibPRP4v4FTg+JTSjCYvtQlwWNW2taq2Taxx3juADcnJ53cOUMbpwNvIXcNeSQ6CHgcmAaeklG5vsq4jUr9heM0BkSRJUhO6LgABSCk9Chze5LFRZ/skciAw2LL/TFVrSoNjTwROHGwZ3cJ5QCRJkjRYXZUDopFldG9lDogBiCRJkgZmAKKWOQyvJEmSBssARC3rn4TuKFiSJEkamAGIWmYOiCRJkgbLAEQt6z8PiAGIJEmSBmYAopY5D4gkSZIGywBELevrNQdEkiRJg2MAopY5EaEkSZIGywBELRvdaxcsSZIkDY4BiFpmErokSZIGywBELXMeEEmSJA2WAYha5jwgkiRJGiwDELWsXwuISeiSJElqggGIWja61xwQSZIkDY4BiFpmDogkSZIGywBELTMHRJIkSYNlAKKW9W8BMQCRJEnSwAxA1LLRBiCSJEkaJAMQtayvMgl9kTkgkiRJGpgBiFpmFyxJkiQNlgGIWjbaJHRJkiQNkgGIWtZvFCwnIpQkSVITDEDUsr5RTkQoSZKkwTEAUcv6ep2IUJIkSYNjAKKWmQMiSZKkwTIAUcuqR8FKyVYQSZIkNWYAopaN6gl6ijSQlGDxEgMQSZIkNWYAorb0bwUxAJEkSVJjBiBqi3kgkiRJGgwDELWl/0hYBiCSJElqzABEbRk9ygBEkiRJzTMAUVv6eismI1xkDogkSZIaMwBRW/rMAZEkSdIgGICoLXbBkiRJ0mAYgKgt1ZMRSpIkSY0YgKgtfaMqckAMQCRJkjQAAxC1pV8OiEnokiRJGoABiNoy2nlAJEmSNAgGIGqLOSCSJEkaDAMQtcUcEEmSJA2GAYja0n8eEHNAJEmS1JgBiNrSbx6QRbaASJIkqTEDELXFHBBJkiQNRlcGIBGxXkT8PCKeiIj5ETEtIk6JiNUHcY23R8RJEXFZRMyIiBQR1wxwTmqw3NDgvHdHxJSIeCEiZkXE3yLisME855Gqr7ecA7LAAESSJEkD6B3uCgxWRLwGuA5YC7gIuBvYAfgMsHdE7JxSmt7EpT4J7AfMA+4Hmg1eHgYm1dj+WJ36fgr4ATAd+DWwADgImBQRW6eUvtBkuSNS/3lADEAkSZLUWNcFIMCPyMHH0SmlH5Q2RsT3gWOAE4Ajm7jOicCx5ABmfeChJsufllI6rpkDI2I88N/ADGC7lNK0Yvs3gRuBz0fEBSml65sse8TplwNiErokSZIG0FVdsCJiY2ACMA04rWr3N4DZwKERsdJA10opXZ9SuiultLjjFS07AhgD/LAUfBRlPwd8u3jYTLA0YpkDIkmSpMHothaQPYv15JRSv7vdlNKLEXEtOUDZCbhsGdVhtYg4AlgHeAG4OaVUL/+jVN9Lauz7c9UxXckARJIkSYNEukeuAAAgAElEQVTRbQHI5sX63jr77yMHIJux7AKQNwD/W7khIm4HDk0p3Vl1bN36ppT+FRGzgfUiYsWU0pxGhUbEzXV2bdFctZcNk9AlSZI0GF3VBQtYtVi/UGd/aftqy6j87wM7A2sCKwPbA+eTg5LLI2LdquObre+qdfaPeP3nATEHRJIkSY11WwvIQEpfxy+TO+GU0uerNt0EHBwR5wMHAl8gJ8I3q+n6ppS2rXmB3DKyzSDK7Ci7YEmSJGkwuq0FZKAWg1WqjhsqZxTrXau2N1vfmR2v0RAxAJEkSdJgdFsAck+x3qzO/k2Ldb0ckWXlmWJdPfpW3fpGxKuK4x8bKP9jJOsbZQ6IJEmSmtdtAcgVxXpCRPSre0SsTM7PmAvUnZV8GdmpWD9Ytf3yYr13jXP2qTqmK43udR4QSZIkNa+rApCU0gPAZGA8eSbzSseTWxR+mVKaXdoYEVtERNsjRUXENrXmF4mI15MnP4Q803mlM4H5wKeKSQlL56wOfKV4eAZdrH8Sui0gkiRJaqwbk9CPAq4DTo2IvYCpwI7AHuSuV8dWHT+1WEflxojYBfhI8XBcsd40IiaVjkkpTaw45WjggIi4HHiUHFhsQW7dGAX8FDinsoyU0kMR8f+AU4GbIuI3wALgIGA94KRungUdzAGRJEnS4HRdAJJSeiAitgO+Sb75fyfwL/JN/vEppRlNXmoT4LCqbWtVbZtY8fOF5KTx15MnDxwLTCdPKPjTlNIf6tT3BxExjTxC1ofJrU7/BL6aUvpFk3UdsfoqumCZAyJJkqSBdF0AApBSehQ4vMljo872ScCkQZR5ITkIGbSU0sXAxa2cO9JVJqHbAiJJkqSBdFUOiEaefjkgJqFLkiRpAAYgaos5IJIkSRoMAxC1pTIAWeAoWJIkSRqAAYjaMrrXHBBJkiQ1zwBEbekzB0SSJEmDYACitpgDIkmSpMEwAFFbDEAkSZI0GAYgakvlMLzzTUKXJEnSAAxA1JY+k9AlSZI0CAYgaotJ6JIkSRoMAxC1pben3AKyeEli8RKDEEmSJNVnAKK2RES/PBC7YUmSJKkRAxC1rW+UeSCSJElqjgGI2tbXax6IJEmSmmMAorY5F4gkSZKaZQCitlXmgCxwLhBJkiQ1YACitpkDIkmSpGYZgKhto80BkSRJUpMMQNQ2c0AkSZLULAMQta0yAFlgACJJkqQGDEDUtn4TEZqELkmSpAYMQNS2vt7KJHRzQCRJklSfAYjaZg6IJEmSmmUAoraZAyJJkqRmGYCobaNtAZEkSVKTDEDUNicilCRJUrMMQNS2fjkgi0xClyRJUn0GIGpbX685IJIkSWqOAYjaZg6IJEmSmmUAoraZAyJJkqRmGYCobf2G4XUmdEmSJDVgAKK29Z8HxCR0SZIk1WcAoraN7jUHRJIkSc0xAFHb+uWA2AVLkiRJDRiAqG19joIlSZKkJhmAqG3mgEiSJKlZBiBqm/OASJIkqVkGIGpbX6/zgEiSJKk5BiBqmzkgkiRJapYBiNo2ut9EhOaASJIkqT4DELWtz3lAJEmS1CQDELXNJHRJkiQ1ywBEbTMHRJIkSc0yAFHbKmdCdx4QSZIkNdKVAUhErBcRP4+IJyJifkRMi4hTImL1QVzj7RFxUkRcFhEzIiJFxDUNjl83Ij4dEX8uypsfEdMj4tKIOKDOObsX1623fKeV5z/S9GsBWWQLiCRJkurrHe4KDFZEvAa4DlgLuAi4G9gB+Aywd0TsnFKa3sSlPgnsB8wD7gcGCl4+DXwReAi4AngS2BA4AHhbRJycUvpcnXOvBKbU2F434Okmo01ClyRJUpO6LgABfkQOPo5OKf2gtDEivg8cA5wAHNnEdU4EjiUHMOuTA4tG/g7snlK6snJjRGwJ3AAcExFnpZRurnHulJTScU3UqSuZAyJJkqRmdVUXrIjYGJgATANOq9r9DWA2cGhErDTQtVJK16eU7kopLW6m7JTS76qDj2L7VOA3xcPdm7nW8qYyB2ShOSCSJElqoKsCEGDPYj05pdTvq/aU0ovAtcCKwE5DXK+FxXpRnf2bRMSnIuIrEXFERGw6VBUbCv0mIrQFRJIkSQ10WxeszYv1vXX230duIdkMuGwoKhQRqwAHAgmYXOewDxZL5XkXAB9NKT3XZDm1unYBbNFkVZcZu2BJkiSpWd3WArJqsX6hzv7S9tWGoC5ERAA/A9YGTi+6Y1V6BvgSsDWwMrAmsA9wKzlouTgiuu13sJR+M6E7CpYkSZIa6LYWkIGUkhGGKhHhJOBg4GpgqRGwUkp3AXdVbJoFXBIR1wG3ATsD+5JH82oopbRtre1Fy8g2g655B5kDIkmSpGZ127fvpRaOVevsX6XquGUmIr5HHnXrKuCdKaX5zZ6bUpoJnF083HUZVG9I9fX0zwFJySBEkiRJtXVbAHJPsd6szv5Scne9HJGOiIiTgS+Q5wPZJ6U0q4XLPFOsBxyxa6Tr6Ql6e2wFkSRJ0sC6LQC5olhPqM6diIiVyV2a5pLn5ei4yE4DPgtcCrwrpTSnxcuVRup6sCOVG2YmokuSJKkZXRWApJQeII80NZ48k3ml48mtCb9MKc0ubYyILSKi7ZGiioTznwBHAX8G3pNSmjvAOTvXSjKPiA8BhwALgPPardtI0D8PxABEkiRJtXVjEvpRwHXAqRGxFzAV2BHYg9z16tiq40sjU0XlxojYBfhI8XBcsd40IiaVjkkpTaw45evF8XPJCeRfyjFJP7ellC6seHwW0FMknT8GjAW2B3Ygzxny8ZTStIGecDcY3etcIJIkSRpY1wUgKaUHImI74JvA3sA7gX8BpwLHp5RmNHmpTYDDqratVbVtYsXPGxXrFYAv17nmL4DKAOR04G3krmGvJAdBjwOTgFNSSrc3WdcRr38XLHNAJEmSVFvXBSAAKaVHgcObPHapZopi+yRyINBsmRPpH5A0c86JwImDOadb9QtAnAtEkiRJdXRVDohGLnNAJEmS1AwDEHVEZQuIOSCSJEmqZ8gCkIhYPSK6fs4L1Tam1xwQSZIkDayjAUhE7BUR342I1Su2rRURVwLPAjMi4vudLFMjg/OASJIkqRmdbgH5NHBASum5im3/DbwVuB+YDnwmIt7b4XI1zExClyRJUjM6HYC8Abim9CAiVgAOAi5NKW0ObA48ChzZ4XI1zPqcB0SSJElN6HQAshbwRMXjHcmT700CSCm9CPyRHIhoOTK63yhY5oBIkiSptk4HIPPJE/WVvBVIwFUV22YCa3S4XA0zc0AkSZLUjE4HIA8Be1Y8PhC4L6X0eMW29ckJ6VqOGIBIkiSpGZ0OQH4BbB0Rf4uIq4GtgbOrjtkGuKfD5WqY9ZsHxCR0SZIk1dHb4eudDuwEHAIEcDFwYmlnROwAbAmc0+FyNcxG95oDIkmSpIF1NABJKS0EPhARR+aH6cWqQx4E3gRM62S5Gn52wZIkSVIzOt0CAkBKaWad7c9i/sdyyQBEkiRJzej0TOirR8RWETGmavvhEXFRRJxddMPScqZfDogBiCRJkurodAvIt4EPkecDASAiPg2cQs4JAdg/IrZLKf2zw2VrGPWbB2SROSCSJEmqrdOjYO0MXJZSmlux7QvA48CuwHuLbZ/rcLkaZv1bQBYPY00kSZI0knW6BWRd4LLSg4jYijzvxxdTStcU2w4mByNajvT1VuaA2AIiSZKk2jrdArICMK/i8c7kmdD/WrHtAXKgouWI84BIkiSpGZ0OQB4Htqh4/A5gJnB7xbbVgcouWloO9MsBMQldkiRJdXS6C9YVwGER8SlyS8h7gAtSSpV3pJsAj3a4XA0zh+GVJElSMzrdAvJfwCzgf4CfkIOQ40o7I2ItYDfgug6Xq2HWPwAxB0SSJEm1dXom9Ici4rXAQcWmP6SUHqk4ZEPgNODsTpar4VeZhO48IJIkSaqn4zOhp5SeBH5YZ9+NwI2dLlPDr/88IAYgkiRJqq3jAUhJRPSRE9JXA14ApqaUFi6r8jS8zAGRJElSMzqdA0JErBIRZwDPA7cBU4Bbgecj4oyIWK3TZWr4jXYeEC0L91wC1/0Q5r843DWRJEkd0tEWkIhYBbgWeC3wInA18C/gVcAbgY8Bu0TEW1JKMztZtoZX/5nQbQFRBzx1F5xzSP559jPw9uOHtz6SpJFnyRLo6fj36VrGOv0b+zI5+Dgd2DCltHtK6f0ppd0pJ6BvVRyn5YhdsNRxj/69/PMj1w9fPSRJI9Md58F3NoCzD4HFi4a7NhqETgcgBwA3pJQ+mVJ6vnJHSumFlNKngeuBAztcrobZaAMQddoLj5V/nvHQ8NVDkjTyLFkMk78GC16Eey+Be/883DVqbMkSePh6ePHJ4a7JiNDpAGQDcs5HI1cC63e4XA2zvt7KUbDMAVEHVAYgs5+GBbOHry6SpJHl4etgVsXN/G0jfIaHycfCmXvDD7eHJ24d7toMu04HIHOAtQY4Zs3iOC1H7IKljqsMQACemzYs1ZAkjUD/uKD/4/smw6xnhqcuA/nXHXDD6fnn+TNzl7HnH2l8DsCz98OTd0Ja/r7Y7XQAciNwcERsWmtnRLwGeC/OBbLcGW0SujrthUf7PzYAkSQBLF4I/7yo/7Yli+DO3w5PfRpJCS75MlARRMx6Cs46GOY+X/ucJUtgyonww23hjF3gx7vC7efCogVDUuWh0OkA5HvAOODGiPhWROwZEVtGxB4RcTw58BgH/HeHy9UwswVEHbVkMcx8ov82AxBJEsCDU2DujKW3j8RuWHf/ER6+Jv8co2DU6PzzM3fDbz60dFCxYDb89jCY8u3ytifvgN9/HE7ZGq76HsyePjR1X4Y6GoCklC4DjgLGAl8BLgX+AfwV+BqwEvCplNJfO1muhl9f5UzozgOids16GpZUzVtqIrokCfp3v9rmw9C7Qv75qTtzd6dWzJkBj98CMx6EeS90ptvTovk5Ub5k+4/Afj8qP552Nfzh0+Wynn8E/vcdMPUPta8360m4/D/h5K3g4s/AM/e0X8dh0vGZ0FNKP46IPwOHAm8CViXPhH4r8OuU0sOdLlPDr69yIsJFtoCoTdX5H2ALiCQJFs6DqX8sP9728LztzvPy49vOhle9fnDXfOZe+N+3w7yKLlE9vbDCGrDiK2D18bDLMbDBjoO77t9+DM8VX56NXQ12/xKsuAa88Ahc9s28/Y5zYbUN4DV75BaRORWtGzt8HHb9AtzyS/j7T8tJ94vmwc2T4M4L4Av3wugVB1evEWCZzNySUnokpXRCSumglNLbi/UJKaWHI2JsMWGhliPmgKijqvM/oPxHXJL08nXf5Dz0LsAaG8Or3wRv/EB5/53nDS5XIiX40xf6Bx+Qc0pmPw3PTM1D/E56F9zyq+avO+uZ3F2qZPcv5+ADYJfPwTaHlfdd9V2Y9O5y8NHTB/ueCu/8LoxbKwchn70T/u0n8Ko3lM970we7MviAZRSADOB0oEbHPXUzc0DUUbVaQJ5/JOeGSJJeviq7X73uQIiAjXaFVdbL2+ZMz0FKs+7+Izx0Zf45emDVDaBvpaWPW7IQ/vAp+Muxzf0vmvLtPOIVwCs2he3/vbwvAt71fdjkbeVtqbjmSmvCYRfDthUBCkDvaHjDIfCxK2Hin2CLd8OOH2/+eY4wwzV3fQx8iLrJqJ6gp/itLkmweIl5IGpDrQBk8QJ48V9DXxdJ0sgw/0W49y/lx687KK97RuWb85Jmk9EXzoW/fKX8ePuPwDF3wrFPwLFPweemwr//FdZ+XfmY638I57wf5s2sf92n7spdpErecQKM6ut/zKheOHgSrLN1eds6r4ePXgEbvrn+tSNg/M7wvrNyC1CXGq4ARMshW0HUMbUCEDARXZK6UUp5cJEHp8ANZ+QE6gs/OfjcvnsugUVz889rvRbW2qK87w0V3bDu+wvMfnbg6117ank+jhXWyN2kSvrGwiqvhvW3hyP+Apu/s//1/3dC7f9JKeWgJhX3Qa/ZEzadULv8MSvDh34POx0Fe341l7Pay2Ou7o4noevla/SoHuYXCejzFy1hbN+oYa6RulZlDsjq48v/pJ6bBhu9dRgqJEkalAWz4bof5u5NT0+tPWzuY3+Hj18FfSs0d81/nF/++XUH9N/3yk1g/R3h0b+V5wTZ6RP1r/X8I3DN98uP9/p6OUej2phxcMhZcPk34ZqT87ZnpsLP9oLN9s4tMD29OXdjwewcaEHu0jXhhNxqUc+4NWHv/6q/fzllAKKO6evtgfn5Z1tA1JaZj5d/Hv/WigDEFhBJGvEeuAIuPnrg2b6fvRf+ehzsc+LA15wzA+6/rPz4dQcufcwbP5ADEIDbzmocgEz+ah5NCnLXp20+3Lj8nh5423Gw5hZ56NzFC3K+yW1n1T9n28Nh7a0aX/dlyi5Y6pi+UcEreYEDe65iyXRvFNWiBXP6jwSyfsWwhw7FK0mDc8dv4fdHwrRrln1Zc5+Diz4Jv9p/6eBj9DhYdzt404dg24nl7X87Ax64fOBr3/3H8vxQ624La2y09DGv/TfoHZt/frLBnCAPTuk/k/o7v5dbMZrxhvfBYX/MyeKNrLAG7PGVxse8jNkCos5YvIj3pz9xxJizWSXmsvg3F8DRt8BYR1zWIFW2fqzy6v5JdgYg0vJr2rXwxC35m+ixqw53bYbGM/fCjT/L/f6XxfO+/kfwlyKv4fZzYY9j4a2fz9/m17JwHlz933D7b/Ixr9wcXrkprLl5/nnNzWCF1Wuf+88/5OFsZz1V3jZ2tdxqsMleeZSqUrkpwYtP5eFtAS48Cj5xXf0uUAB3Vna/qtH6Afn123Lf3P0K4PZzlp4TZPFC+POXyo9ffwhssFP9cmvZYEf49M05cFowO3f5Wrwwj461ZGHuerXZ3rDSKwd33ZeRtgOQiHBczJe7R/8O//c5PrvwzpfGNxs155n8B6By2DmpGZX5H6uu3/9bLpPQpeXTTWfCHz+bf77rQjjikqVHDVqeLF4I15yS539YXMxZceV3YYePwo6fyHkB7brtnHLwAUCCK/4THr0BDvjp0jf7067N3aam31/e9ty0nHBdqXdsbs0Ys3LOjRi9cr7pfuzG/sdttX9uWRi31tJ1i4D3nAo/ejPMeTaPcPh/n4ODzqydL/HiU3nW8Hxybumo540fqAhAzoVXbJK/yFpjo/w/5cb/zfkbkIfbfdvx9a/VyNhVG9dDDXWiBaSVIXUdo3V5MPtZ+Os34NZf195/85mw3RGNk6+kapUjYK26HoxbB0aNgcXzcxLjvBdePt+OSi8Hf/sx/Pk/yo8fvynPEj3hW8NXp2Xp8VtyDsFT/+i/ff5MuPqk3Gqx7WHwlk/nv4GtuPtPuStUSd9KsHB2/vn+v8IZb4X3/gLW2y7/Tf3rcXDTz5u79qJ5eZlTZ5SpcWvDu07KLRGNjFsL3vMDOPf9+fFdv4fN3wWvP3jpY/95UXlUqQ13zq3j9Wy0G6yybm5NnzsjBzYlPVVB7W7/D1Z5VeN6aploOwckpdTTwtLW8EgRsV5E/DwinoiI+RExLSJOiYg67YI1r/H2iDgpIi6LiBkRkSJiwA6SEbFVRJwXEU9HxLyIuCcijo+IukM4RMRbIuJPRTlzIuKOiPhsRHTnMFFLFuc/VD/Ytl/wMY8xnLLoAOam0XnDk3fmP7TSYFQHID09eSSsErthScuPa0/tH3yUXHcq3DuIyeS6wYI5MPlreeSkyuDjVW/I39KXLJqb8yL+541wwUfzzff8F5sv56Gr4bcTyxPbrb01HPMP2OWY8jEzH4Of750n1Tttx/7Bx+iVcwDxietyi8TuX4bXHpDnwhg1pnHZbzoUPvm3gYOPki3e2T/5+/8+D89XtII/fXdOFp/y7fK26tGvqvWMgh0+VnvfkoXlPJI1XpOHv9Ww6LockIh4DXAdsBZwEXA3sAPwGWDviNg5pTS9iUt9EtgPmAfcDwwYvETEjsDlQB9wPvAosCfwdWCviNgrpTS/6pz9gAuKcn5DngV+X+BkYGegRqg/wt1+DvzxmP7btng3Rz9zIJMfH8O6PMvBvVfl7Tf/HNbbdujrqO5VHYBADkCevSf//Ny0/A9bUne78nu5S1DJ+jvmrj0PFCMd/f7jcOQ1sOq6w1O/RpYszn3/m8lzXDQfpl4MV5wAMx4sb+9dAfY8Nne5isjHXH0SPFkkTi9ZCHeel5eePhi/C2y+D2z2jv5fylR64tY8Sd7i4lZk9Y3gQxfk7lZvOy6/xr//eG71WLIwT6pXafN35uCj1MKw9mv7708JFs6B+bNgwazcalP6+RWb5qFwB+sd34aHrsp/2+e/ABd+ArY+KH/BWd2tq6cvd+0ayFuOznV/4rb8mj/3UF6X8lNGjYZ3nwy9AwRUWma6LgABfkQOPo5OKf2gtDEivg8cA5wAHNnEdU4EjiUHMOsDDTuXF60VZwIrAvullP5QbO8BzgMOLMr/TsU5qwA/BRYDu6eUbiq2f40cyBwUEe9LKZ3bRH1Hjq3fm7+1evae/Edwn+/CZu/g+R9fD8zg7MV7lQOQf/wu/3Gxy4yaVZ0DAraASMuTlPLN+FXfK28b/1Z4/7m5a88Zu+ScgLkz4HcfhQ//Ic8aPVLMmQG/PjAnzK+5Zf62f8t356FcK7scz3goz4Z966+X7q40/q05B6JykI3X7g9b7ZeHmr36JHjkuvK+JQvhwSvy8uf/yH8T19gYVtsQVt8wr8esnIOLBUVrycqvgg9fCCuvXb7O5vvkeTfO+zD86/by9pXWzPkaW+3fuNt0BIxeKS+sXf+4wRizMvzbj+HMfXI3q2lXV+R7VFj5VbDXN2ClVwx8zZ4e2PTteak0fxY8/zCs+Mr+r4uG3Aj6RA8sIjYGJgDTgNOqdn8D+BhwaER8PqU0u9G1UkrXV1y3meJ3A7YErioFH8V1lkTEf5ADkCMj4sSUUinH5SBgTeCXpeCjOGdeRHwVuAz4BNBdAUjv6PwNybRrYJfPvjSB0Jje3KPv1rQJs1bbgnHP352/KbnjvJxYJzWjVguIiejS8iGlnDt47f+Ut228B7zvbBi9Yk5qPvBn8It9883ow9fmRO2RMpzpwnm5heGJonvxM1PzctV3YbUNYIsiEPnH+cWcFVUpr2NWzbkt23y49o1+BGz6trw8fgvc86c8+/dTd/Y/7rlpjb+MWWF1OPT3tVtKVh8PR0yGy47PeRebTsitI41GoFrWNtgpdxG7+qT+23v6cjetNx2aZxRvdqjcesaMW7pVR8OiqwIQcncngMkppX4z3aWUXoyIa8kByk7km/tlUfYl1TtSSg9GxL3AZsDGwAMDnQNcBcwB3hIRY6q7bo14G711qRmp+0aVUoqCxzY+hC1uKUaWuOnnsP1HTEbXwJYsgRcqhuEtdb2wBURaPtz6q/7Bx6YT4L2/gr6x5W3jd4HdvlTu93/ld3Pi8ca75ccp5ZbSx2+BZ+7JN6VjVi6+mR9XHplp7a3y9k5Zsji3yDx6Q+39zz8CN/yo9r5V1s1zX2w7sfaoULWsu01e9vxq/mLm3kvg3r/Ag1eWu1jV0rcSfPB8WGvLBseMzbNvj6QZuHf7Uu4y9cBlsNZrYZtDc4+LZlo81HW6LQDZvFjfW2f/feQAZDM6H4A0U/ZmxVIKQOqek1JaFBEPAa8lBy1TGxUeETfX2bVFo/OGUt+ocoDx2PrvZos7v5dbQJ7+Zx6qd4MdG5wtkbsplP6xjl2tfPOwekULiLOhS/3NfCInK897Hv7tDFhn6+GuUW3PPQyXVAwLu8W7c5Jz7+ilj931CxVdcVK+8d/28Jzj8PjN9UdgqjR6XP7mfKdP5G5K9Syan/9HjV4RXr1N7S/LUoK/fAWm/qG8ba9vwCteA1P/mIOD+TOrTgrY5G15NMhNJ7TXjWzV9fIXedt/BBbOhekP5K5Ezz3cfx09eVbx9bZrvazh0js6B04LZ+ffnV9aLte6LQApJRK8UGd/aftqI6Ts4azvkCu3gMCcnnF5oqBbf5U33HymAYgGViv/A3LXhpLnH4XFi0ZWn3BpuMx/Ec5+bx51EODcD8DHr4YVlsG/lSWL8w1uKzeGS5bkYWEXzMqPX7FJnouiVvABuVXjwJ/B6TvnYGPWU3Dld2ofW8+CWfC30+HvP865DW/5dG5RAJg3E+6/NAcP911azpt49Ztyi8Nr9ur/PK8/LY9MVbLTUfDWYnjXrfaDRQtg2lX5etPvzzN1bzux9mzd7epbAdZ5XV6WNz09nW210oi1vP0HL/21GI55Rlopu+lzUko1h5IqWka2GUSZy8zoigBk4aIlsN3h5QDkrt/npt56M6hKUDv/A/I3k+PWgVlP5qElX3h02fxjl7rJ4kVw/hHl4ANyN6A/fCp3a2r3G+RFC+Cxv+fZnh+4PHeP2WCnHDistv7A51e68aflxOLogf3PyJ/rRlZeBw74Cfy6xrCrY1aBV78xj4gXo/KIVAtKIzMVicalEafSErjrd3nZcGfoWxEeurI8AWClJ27NCeYb7gx7fg02fDP84wKYfGz5mK32gwkn9D+vd3Ru7djkbc2/JtLLWLcFIKUWg3pDKq1Sddxwlz2c9R1ylS0gCxcvyU3Z67w+Dym4aF6ekXSnTwxjDTXi1QtAIAccs57MPz83zQBEL28pwSVfhPtqzJUx9WL4+09hxzpzIVRbsgTmTM8jT816Kn+D/+CUPJ/EwqrxXB65Hn6yGxw8CTbatbnrP3s/XPqN8uNdjoH1t2/u3E32ygHPnefnXLB1t8n/W16xSf62vJ6UchL4dafmYKPk4WtrH7/aBjDr6fy/qnTcmXvnSe0eub583AZvhn/7SeOyJQ2o2wKQYiIANquzf9NiXS9PY6jLvgfYrjinXw5HRPQCGwGLgAdZDvT1lr9tW7h4Sf72bbvDy3OG3HQm7Hik/TpVX6MAZPXx5RsBE9H1cnfDj+DGn5Uf7/K5nINQ2jb5WFh/h0JoBeoAACAASURBVNxKUG3xQrjh9DzBXSnoWLKo+bLnTIdf7g9v/ya8+ZON/6YvXgQXHpkn14M8md1uX2y+LIDXvzcvg1E5mtS/7sjzXfzjgv7Pc52tYYtiCN21toIXn8xDA9/yi/JxlcHLKzfLo3VVJsxLakm3hfBXFOsJxfwbL4mIlckT+80F6gxR0ZbLi/Xe1TuK4YE3Ax6mfzBR9xxgV/KcItd13QhYdVS2gCxYXPQq2/rgnEwGed6Qym+SpGr9ckCqA5AuTERPKc98/IPt4J9/GPh4qRlTL84zWJe89oDcXWjCCeUE9MUL8mzY86oSo/91B/x0D7j0a/D4TTDz8cbBx2ob5uTv9/4qT2i3UjGCU1qcg5wL/j13f6rnulPLk8n19OUk+aGe/O1Vr89duT5zO7zjv2Cf7+Wfj7wGdv9iHpY1AlZ5Fbz7+/Cpm+D176PcSxoYt3ZOkB7OoWql5UhXBSAppQeAycB48kzmlY4HViLPufHSX8OI2CIiOjFS1JXkkap2jYj3VFy/hzypIcAZFXOAQJ4t/VngfRGxXcU5Y4HS9K+nd6BuI8Lo6i5YkJPJtj6ofNBNZw5xrdRV+rWAVPUx78aheP95Ub4Bm35fTsCtvhmUBuuxm/OIV6XUwfV3gv1Pz12C+sbCwb8of+nz3ENw8dE5EF40Hy4/IQcfT9659HXHrgZrbgEb7w5veH+e6+noW+Gzd8C+p8BW78n5DR+/Etar6D71jwvgZ2/P11xUlVPx1F1wxbfLj3f/4vCO0LXqevDmo3LXtHoziUPu3nnAj+Go6+ENH8gjWB16YeORtCQNSrd1wQI4CrgOODUi9iIHBTsCe5C7Px1bdXxpeNt+bcQRsQvwkeJh8deaTSNiUumYlNLEip8XR8Th5FaN8yPifOARYC9yN6trgZMry0gpzYyIj5IDkSkRcS4wA3gPeYje84HfDO7pj1x91UnoJdsdkWeDhXxD9vbjYZVXD23l1B0GygEp6YbJCBfOg0u/Xn48f2YelOHN1d+daLmweCFceFQeWWnNLXOy9gZvzrkOnRp847lpcM4h5e5Ma2y8dJegV/x/9u47PK7q2vv498xo1LtkWc2W5N57xRhjQoyphkAIvVwgQJJLbhLS3iSQHpJ7QxJII0BC7z2hg01zxb03SZYt2ZKs3qXRzHn/2CONJI+Kbcm2rN/nYZ7RnD7y2Jw1e6+1hsPFfzIjE2AKgMSkw54PTMO8FkGhprnfuCXm231fQ9luRafCTW/COz8wPZ4AireZ7uVYpqN2dIrpe3F4p+ngDSZvY963jufdn3hJY+Gy0+Y7QpFTSr8LQGzbzvaNJvwcM7XpAuAQ8ADwM9u2y3p4qBHAjR2WJXVYdlOHc6+2LGsmZrRlERCFmXb1c+C+QFOpbNt+zbKsBZjA6HIgFNgLfBt4oMOISb92RBJ6i5TJ5n8+B9ebHg8v3wo3vKEyqtKeux5qD5ufLaepgNNWxxEQ2z6184lW/91U4mlr1d9g1u367J+O3vkhbHnB/Lx/hXkAYJkb2SGzzWc4Ktnc8EcONj+HxfXsc1xZAI9f4v87EhZnpgQFatI28QpTcarli58VD7ZfP2QOLPkLJI44hjeKmUJ10R9Mydo3v9OmmpQNtcXmcWhTm+1D4bKH9LkXkVb98l8D27YPADf3cNuA/7Lbtv0Y8NgxnHs78OWj3Gc5JlA6rbVNQm/NAWmx6Bfw+MWmHGLeclPP/Zwfn+ArlFNa1UH/z9Fppg9AWxGDTIdfd60ZTagvP3XnY9cUwyf/12aBBfi6N29/rf20RDk2Xq/5DJwKXZLXP2HKzAZkm2asxdsDrw4KNT0qzr+v85GSmmJ4Yok/oHWGmJGPhOGdX9Pi+yB/LRRt9S9zhZvmebO+2jtVnKbdYPInlv4SirabZPZAVeXP/SkM6qx+i4gMRP0yAJFTU8AckBaZZ8LZP4Rlvtrpn/wfZJwBw885gVcop7SuEtDBfEscl2mme4CZ336qBiDLfuVvbJY4CsZeAp/6ApIVD5omnafy6M2pzuuF56423afHXgxfeqT7ykTZy+DT35vS4Ofe23uJ0PtXw3++7X89bglMuAL2rzKjIIc2m4TtzjQ3wObnIG8FXPGoqVzVVl2ZqThVuse8drjgK0+afz+74gozpXL/dYEZkcicD5c82Pvlq9Omw/Wvmp89bhOEVB2C6oPmOXYojD6/d88pIv2eAhDpNe2qYDV7j9xg/nfM6EfOR4BtEinv+MzMFxbpKv+jRbsAZJ+5+ekL7gb46NcmMXfe/xzdt8VF28w34i0W/cr0Llj5Z3OzeWij+XuQeWbvX/dAsf5xE3yAqQj17Ffgqmc7b2y35SV49XZT7WnfpyZ4vfIJcLqO7zqqDsIL1/vzHAZPNAnhwREmaRtMU7yCdebPvbrI9LJpea4pNqN5AJX74Z+L4Qv3wBl3mc9cQ5Vpitfymbccpjv4qPN6dn2JI+Ebn0NtiRkt6eug1+kyf3c7+/srIuKjAER6Tac5IC0cTtNQ6u9nmm/J6kp8+SCva26w9CwAOVGJ6B/8FFb7kk+DQnqeOG7b8O7/M1MNwYzwjfyiufGbfDWs81WBW/Fg5wFIZQFseArSp6urciC1JebPp62cj+CZK+Hq5yAksv26NQ/DW9+l3dSgXW/BK181N/Mdp/r1lLsBnrvWN+0ICIuHq542wUdbIZEwbIF5BLLjP/D616Ch0oyUfHAv5H5icixevd3kzgFgmeBm/KVHd51hseYhInIK6VdleOXU5nK2zQEJEIAARCaZ/+m3tHHJ+ww+/m3gbWVg6W4KFpyYUrxNdbDxaf/r5Q+Ym82e2P2ub4QP8xlf9Cv/t85tg5jd78DhAP1SS7Ph4XPM6MtTl8Pr3zDX0x1Pc9e9GE4n798LDRXm5+Ao//J9n5rfWUupY9uGj38Hb91Na/DRNsdi2yvwxl1mOldnPG4zBapjrRDbNg1WW4IDywlXPn5sZVrHXmRGgtPbTL3K/hAemNK+b9JF98Pkq47++CIipyB97Sy9Jjio7QhIF8W9ss4ynXA/+o15/cn/+vJBFvbxFcox8XqhsdJMR+rLKRxd9QBpcSICkG2v+KfFgJkqs/FpmHlL1/t53PBem8IK02+CweP8rxNHwugLzLfvYKZkXfKAf31ZrinUUFPoX7bhSdPE7Yp/tT9Wi+pCWP4nU+3IXQfhiWaUKC7T/0idahKFTwf7V8HGp/yvr/inSe7+4F7z+sAqePJSUx3q49/5R7HATNe79iXz786af5hlG58yuRIX/G/7z3bJHlPFbOMz5vfqCvdPLYpJNwHfpmf82y/+jfl37VjFDoWb3zLJ3Mv/aJbZbQKj835typmLiJwmFIBIr+m0D0ggZ33XzIPP/QSw4ZXb4LZlENvJjaecHJ5meO4a2PMuzLwNLvy/7vc5Vu0CkLTA27Trhr6vb64jULPM5X80FX+6yhn4/FF/onBINCzs2JIImPsNfwCy6TnTvTpyEFTsN8FHVYFZZzn8N6CHd5rmcef/FqbdaG6Uqw6Za1r3mMkraVFXYh4tnadbTLvBdH/uLlH7VOZpbp/sPeYiGLXIPIJC4Z3vm+UF68zoQUOlf9thZ8NXnjbToRb/1jfK5QtkPn/Y5I584aeQvdQELXs/aH9udx2U7DaPjqZcZ6pKHS+ny/RIypxvpl7VlZjlC3+s3jEictpRACK9ptsckLYcTlO55u9n+urGH4YnLoGb3z6y/4OcPMv/aIIPMDdqoxf3TV6CbfcsByR2CP6Stvmm83JQcO9dR+EWKFhrfnYGm/n89eUmQNjyEky5OvB+NYf9I3oAZ90NEYlHbpdxRvueOJ8/bIKDxy7yT0ELCoVrnoeKAyZ3obneBBn//ibkfGzKEa97zOzfVtugpaP1T5i+DFc+0XUH6L52eJd5H9WFptRyTFqb53RTqjV+WOB91zzkT8Z2hZsysy3m3GE+B//xNbprG3yMvcRM+2ypeuVwmJGn5nrTxRvMKNLmF03lpo6cIUf+rlukzTBTo3pzZHDkuXDncpMHNGgMjLmw944tInKKUAAivaZHOSBtRQ02UyievMxUkSnLMbXub3oz8M2bnFhF2+Cj+9ove+u7cOfK3v8mva7U/01+SDSExgTeLijEBCeVBwDbBAbH2kwtkJbGbWDKuw4aC8t+aV5/dj9M+sqRFbG8HnjlVn9eQlwmzL4j8PEtC874Brzkm06z5mHY8mKb/g7BJpF52NnmdfoMePFmfwfrba8cecyUKXD2D2DkIqg+ZKZyle8zj4J1kLPMbHdoEzy0wBSCGLWop7+R3pPzETx/g5nOB6YSVSAjF5kO3alT/cuqDsKyX/tfL/j+kaOlM/7L/P5e/watOR/TboCL/nhkornDaRrjuev9I1Ltgg/LlI6dc6cZkWioNJ+5ynzf44Aphzv3671XzretqGQTxIqInKYUgEiv6bIPSGey5pta9S/cYCrAHN5p5nDf+O/Om3JJ3/O44dU7/OVFW5TlwIoHYMH3evd8PUlAbxGX6d++fF/vBSBNtbD5Bf/r6TdD8kTzfhurzPSbHW8cWYXo0/v9iedYcOH9Xd+Ujl0CMUNN2dX6MvMAX3+Hp9qPMCWNhduWwjs/MKVn20qd5g88Wr6Bb8lRyJpvXts2fP6I6dLtdZsg6Zkvw1nfM/s6nCaAKs2Gws1mBMhdb95jd30mjsb6J8zohLe5+233vGceYy4ygcjg8aayWFONWT9oDMz5WuB9p15n8mDW/MP8Xmbf3vnohNNlcmuevcofpAVHwbTrYdZt7UdiWipJJU/s+XsWEZFOKQCRXuPqaRJ6R2Mvgi/9w5TkxTY3QU9dATe8BiFR3e4+IJTlmulQQ+aYSjjdTfmoOWwa31kOk29ztA37Pv29uSEFMwVl5i2w6q/+dZOu7N2pPD2ZftUiLsNUPILOv0U/Fltf9iefJ4wwZXItC2beakY/wLz3cUv8v//cT03FqhbzvwMjvtD1eZxB5pv1d3/oX+YIMlWUAvV3CA43U4aGLYBlvzHfjs/7pglUuvscWJa5mU6ZAi/e6M8x+eR3Jt/BssxIl7tDpa01D5mb/C/cY5K0j5XXC0t/Dp/9wb8sKgWW/BmwzPVUFkBVPpTnwb7PaB292Pkf8xi20B8gAFz4+66n3Y1ebB494Qo1pXs3PGmCxvGX6d8cEZETQAGI9JpjGgFpMfEKMwXndV+yZcFaeOYrpmpNx+ZiHreZ694XUx9ORc2N8PQVULrXTBE6uMFU3emsf0H5PjOtrSzHvM5bYYK5no4oHdpkKpO1+MJPzM1o3nKzrrkB3v4BXPPc8byr9o4qAOmjRPS206+m39S+fO6qv5mcgcLNsOd9M4WpphhevsWfd5ExD87+YcejBjbtehME1JebEq5X/LP7uf4TLjePYzFkJtz+ibneltGallyXzqz6q0nGvuwh00gxEI8biraaACoqxfTCaJmi5q43ydTbX/dvnzwRrn6+8yIDxTtNLs321/zL2gYfk67q/QaOrlATpImIyAmjAER6Tbed0Lsz9Tpz0/KWb+5z3nL41/nmhrTWV92ntsQ3194yZU1TpkDKZEidYm5uQmNM3f7DO6F4h/+5utAcf943+74bcG/77I8m+Gix5iFTqvWyfxyZi1G4FZ76kr85GpgOzE9+yQQhneVWtGhuglfv9E+VGTLHBB8Op5la9Mi5gA2734Zdb5t58r3hqAKQTP/P2ctM743MM49sAAemf8eBVWa7Q5vMtJw5dx75GTi02eRLgMkjmHyNf11EIsy42T8C9Mn/mlGOV27z/57DE+HyR3veUDMkCq55wSQaT7zi+Eq49lREIlz3isml+LRDNbPIZPP3J3miCSj2vGeWl+w2f+YLvmdGd5wuE3jted8UJ8he1r5ksSMIIgebR2NV+8/tyPNMoNWxUWBbSWPMSNChzSYQacnPAAiJgUW/OP7fg4iInHQKQKTXBAf5b+qOegSkxazbTBDy/k/M60MbzeMItr8s5pY28/bD4sy3yoF8cK9JWr7gf4+9+/GJVpptpv10tP11qC01CcstXY73LYdnr/Yn+TqDwdNkfj643jRpu+4VCI3u/Hyf/M5faSgoDC79q/93lT7DJPW25CK8/T3IWnDkCNWxaJcD0k0p5rbd0Iu3mQ7YzmAYOtcEBi1VprKXmUZubcvU5iwzU/wu/lP7aTzr2pTeHXsJRCS0P+cZ/21yKTxNkL8Gnr++fd7Hl/4B0SlH845hyCzzOJEcTjOiNeJcM5I2aBQkTzINQlvYtsnZaMm7sD0mGNj5H5On0tqZOwBvs5lW1TLVq8XsO0wvi57+vUuZBFc/a4LCT34PZdlw3q/aX6eIiPRbCkCk17Qvw3sUOSAdzbvLzElvW9a0Hct8gx2o5GhnwUeLtY+abS57qHfLt/YF24Y3v+0vAZo6zdywrv67eZ33GfzrArjuJShYbyortWwbEm1u4Ep2+0uT5n9upnJd93Lgee4F60xCdYtzfwoJw9tv84V7TSJ2S2naz+6Hc37McTuaEZCUKaZC0sEN/mWeJsj92Dy6s+kZU/HoyidNMNZYY0qwtphx85H7RKfClGv807R2velf15O8j1NNxlzzCMSyYPqNZlTmtTv93bgLtwTePjrdjD7VFLYvfwsmB2nxfSYZ/FikTYern+l+OxER6VcUgEivOao+IN05+weQPtPM8Q9PMNNHwhPNc1icyYso2mqm1RzcaJ4P7zDfwDpDzDe7g8aaKR2DxprypVt8N5nbXjHTuL7yVOBpO6eKLS/5v2W3HHDRH8x0s6gUf+fn4m2mtGpdiT8gixxsgozkiWZqktfjn9Z2YDU8fSVc+6J572U5Jkckb4WZdmN7zHYZ8wI3V4tIMIHJv79pXi//E4w630w9qi405VKrC01lp7QZZnpRT771rmzzjXl3AYjDCbe8b5pYZi+FvR/6y9QGkjAShi+E+gr/aFnOR2Z637UvmulETdX+bTPmBT7OvP+B9U/6f0cAGWf2PO+jv4nPMiWxV/7ZdOhuGU2znDB0jpnONuo8U5WqZUqbu978+dcUmalaiSNNJS8REZE2FIBIr+nVAAS6/lY5OPzIKSzuBnMjHpVy5E3vqMUmcFnzD/M6e6npOXLNC0dfIepEqC9vXyVp9h0mzwXgzP8xlZBe/7oJuGqL/dvFDzPTrNpOU5p1mwlCWjpF718B/zgbGqvNt9YduSJgyV+O7HfRYuoN5ka8YK25KX3knE7exD/MaM1F97fv6dBRc6P/OiyH+fPrjtNlPh8jvmCm5lQWmD/T7A/h8G5z0zvsbPNo6Rdh2+aGeNmvzOuirSa/oW0Q2jb5vKP4LBNQbX7evA5PNA3uepr30R85nCZvauR5sO1VE9gPP6fzggauMPN7avv5ExER6eA0/j+nnGjBx5uEfrxcoZ1/e+5wwPm/MzeNLWVT8z83U5gu/L3Z13KaG2CH0yTTJow4ebkiH/7cdIcHiEo1/RDamnyVGQ16/gZw15plyZPMyEegefJz7jDf3L/rO07pnsDnDY2FS//W9Q2kw2F+Zw8v7LzzdouD6+EfC00p23N+7M9XaauqTQO4qBQTXBytmDRTWWra9Z1vY1kmmTomHd74b3++QgtniJlm1ZVzfmymJDXVmv41R5v30V8ljYGk03SkR0RETjgFINJrXO2S0I8jB6SvWBac/X0z4vHWdwHbTN157ILA20enm5vMITOP7XxVB0351rIcc2M7+oKeVeA68DmsbZMUff5vA+dsjDgXbn7LdCuPSTc9G7pKMJ/rGzF5/x7/spAYM50m4wwz9Shlcs9yY1KnmKlYy35jEsCjU0zwEJ1qRmeaG02Xb08jYMPnD5vSqot+ZXqItP09HE3+R2+Yco25xudv8E+9AtPfo7vRsNih8D9bwNN8eo98iIiI9CHLtk/BG0XpMcuy1k2bNm3aunXrTval4PZ4GfmjtwEIcljs/XUnN/angi0vmR4F3XVmdobAZX87uv4L5XmmaeCGp/zz5gEGT4Sz7jZVljqb3uRpNtOjinwJvyPPg2ue793SwdnLTAO/tBmmy/TxjPLYdufXVpptAr3sD9svjxhk9vM0mUDF00Rr87kJl5tSrSdC4VZ4+ssmIR3gv96DobNPzLlFRET6oenTp7N+/fr1tm1PP57j6Cs86TVBDv+NaLPXxuu1cTh68ca5N028wnwLvuJBM9XJ6zFTlGzb/FyZb74d9zSa6lJlOTD/7q4DgZaSuZufDxzYFG0x3agHjTWByPjLzJSv6kOmX8nhXaYTdEvwERRmSgb3dt+S4QuBhb1zrK6uLWG4mRK24w3TuLDlRr9lalkg8cN657p6InkC3LbUlNdNnqDgQ0RE5ARRACK9xrIsgp0OmnwJ6G6vl5BTud9G5pmdd1UuyzHVolpyJZb+Ekpz4OI/tu/AXldmukXveAN2vnlkTkTaDNNFesNTprQwmGlfL98C7/3E5BI0dihd2uLs70NcxvG9x5PNsszUpuHnmKliqx8Cr/vI7RxBpmrX9AAlcPtSdIrpiyEiIiInjAIQ6VUup0WTr0qp22MT0l8/YfHD4Nb3TcO5fZ+aZZuegYo8041532ew6x3TZTtQInbGmbDgu6ZRn2XBgu+bcqZrHjbN3cA/IhBI6lSY+43ef18nS0iUqVZ19g9Nha+gEJM7EhRiprl1NiVNRERETjv99fZQTlGuIActEYi72Qsh3exwKguLMyVt3/w2bHjSLMtbDg93VnYW803/Wd81Sd1tRSSapO0z7jKJ6av/Do1VZl1IDAwa7XuMMRWHMs48tmpQp7qQSPMQERGRAUsBiPSqtr1ASmubiIs4xbuNdycoGC550JTkbWn+145lujWPXgyjL4TB47o+Xng8nPMj0+29ZLcpsRuV3Pt5HiIiIiKnKAUg0qvGpkRzuNokGT+1Ko+fXjL+JF9RL7As0/wvPst0AG9uMonco8833aAD9d3oTkiUCVxEREREBhgFINKrbjkzi092mwDkuc/389/njCAhsj/Pw2pj3BJfLw/HyWtQKCIiItLPKfNTetVZIxMZn2qa4TW4vTy2Yt/JvaDe5nQp+BARERE5DgpApFdZlsXXzh7R+vrxFfuobghQdlVEREREBiQFINLrFk9IJisxAoCqhmaeWb3/JF+RiIiIiJwqFIBIr3M6LG4/y9/R+pHPcmlwe07iFYmIiIjIqUIBiPSJy6alMTjaJJ8frm7klfUFJ/mKRERERORUoABE+kRIkJNbz/SPgjz0STYer30Sr0hERERETgUKQKTPXD17KDFhppt3Xmkdb205dJKvSERERERONgUg0mciQ4K4cW5G6+u/fpSNbWsURERERGQgUwAifeqmeVmEuUzfjB2HqvjY16RQRERERAYmBSDSp+Ijgrlq1pDW13/9KPskXo2IiIiInGwKQKTP3Tp/GEEOC4A1uWX87p2dmoolIiIiMkApAJE+lxYbxpUz24+C/OT1rXhVFUtERERkwFEAIifEPReN4wtjklpfP7VqP996YSNuj/ckXpWIiIiInGgKQOSECHU5+fv101kyJbV12esbD3LHk+vUJV1ERERkAFEAIieMy+ngD1dO4bo5Q1uXfbizmBv/uYbqBvdJvDIREREROVEUgMgJ5XBY/GLJBL529vDWZatzy7jibyt5alUeh6sbT+LViYiIiEhfCzrZFyADj2VZfG/xGKLDXNz39k4AdhVV8+PXtnLP61uZmRnPBRNTWDwhmcHRoSf5akVERESkN/XLERDLstIty/qnZVkHLctqtCxrn2VZf7QsK+4ojxPv22+f7zgHfcdND7DtTZZl2d08PB32yexm++eO93fRn92xYDi/+dJEgp3+j6HXNiMi976xjTm/+ZArH1rJu9sKVTFLRERE5DTR70ZALMsaDqwAkoDXgZ3ALOCbwGLLsubZtl3ag+Mk+I4zClgKPAeMAW4GLrQsa65t2zltdtkI/KyTw80HzgHe7mT9JuC1AMu3dnedp7urZw1l4egk3tl6iLe2FvL5vjJaWoTYtukbsia3jBFJkdyxYDhLpqTicvbLuFlERERE6IcBCPBXTPBxl23bD7YstCzrfuBbwK+AO3pwnF9jgo8/2Lb97TbHuQv4k+88i1uW27a9EROEHMGyrJW+H//Rybk22rb90x5c04CUHBPKTfOyuGleFsXVDby7rYi3txxiVU4pLQMfe4truPvFTdz/3i5unT+Mq2YNITy4P358RURERAY2qz91pLYsaxiQDewDhtu27W2zLgo4BFhAkm3btV0cJwI4DHiBFNu2q9usc/jOkek7R07Ag/i3nwBsAQqADNu2PW3WZQK5wOO2bd/U83fac5ZlrZs2bdq0devW9cXhT6riqgYeXZ7L06v2U9PY3G5dRLCT9LhwkqJDGBwdymDfc2pMGPNGJBIW7DxJVy0iIiJyepo+fTrr169fb9v29OM5Tn/7Cvkc3/N7bYMPANu2qy3LWg4sAuYAH3ZxnLlAmO841W1X2LbttSzrPeCrwEKgywAEuN33/Gjb4KODVMuybgcSgFJgpW3bm7s57oCXFB3KD88fy9fOHsFTq/L41/JcSmqaAKht8rCrqJpdRdVH7BcVGsTl09K5bs5QRiRFnejLFhEREZEu9LcAZLTveXcn6/dgApBRdB2A9OQ4+I7TKcuywoDrMCMpj3Sx6Rd9j7b7fgTcaNv2/q7O0Wb7zoY4xvRk//4sJszF1xeO4JYzs3hx7QEe/jSX/WV1nW5f3dDMYyv28diKfcwZFs+1szM4b3wywUHKHRERERE52fpbABLje67sZH3L8tgTdJwrfdu8adv2gQDr64BfYBLQW0ZSJgE/xYyufGhZ1pSupouJX6jLyfVzM7luTgaHaxoprmqkuLqBoqpGiqoaKKpqYGV2KftK/cHJqpwyVuWUkRgZwqVTUlkyJY0JadFYlnUS34mIiIjIwNXfApDuCvXEoAAAIABJREFUtNxVHm9iS0+P81Xf80OBVtq2XQzc02HxJ5ZlLQI+A2YDt2KS3rvU2Vw738jItO72P51YlkVSVChJUaH4Y0nD67VZnl3CU6vy+GBHMR5fFntJTSOPfJbLI5/lkpUYwcWTU7lkciojkiJPwjsQERERGbj6WwDSMjIR08n66A7b9dlxLMsaB5wB5ANvdXO+dmzbbrYs6xFMAHIWPQhApGccDov5Iwcxf+QgCisbeHbNfp77fD9FVf4O67kltTzw4R4e+HAP41KiuWrWEK6cMYRQlxLXRURERPpaf5sUv8v33Fluxkjfc2e5Hb15nJ4kn3flsO854hj2lR5IjgnlW18cxfLvn8O/bp7Jl6amEdGhOtb2Q1Xc8/o25v9uGY98mkNdU3MnRxMRERGR3tDfRkCW+Z4XWZblCFCGdx5QD6zq5jirfNvNsywrKkAZ3kUdzteOZVmhwPWY5PNHj+WNYCp1QfdVtuQ4BTkdLBydxMLRSTS4PSzdWcwbGw+ydFcxTc3mI3S4upFfvrmDv32UzS3zs7hhbiaRIf3tr4eIiIjIqa9fjYDYtp0NvIfp0fH1Dqt/hhlNeKJtUrdlWWMsy2pXKcq27RrgSd/2P+1wnG/4jv9uFz1AvgzEAW91knzecu7ZlmUFB1h+DqZpIsBTne0vvS/U5eSCiSn8/frprP3xudxz0TgGR4e0ri+tbeJ37+xi3n1L+dWb2/lwRxGVde6TeMUiIiIip5d+1YgQwLKs4cAKTDf014EdmFyKhZgpU2fYtl3aZnsbwLZtq8NxEnzHGQUsBdYAY4ElQLHvONmdXMOnwJnAJbZt/7uLa/0IGA98hMkVAVMFq6WfyU9s2/5lz955p+c4bRsRnigNbg8vrsvn7x9lU1BRf8R6y4IxydHMyoxjVlYCM7PifAnwIiIiIgNHbzUi7HcBCIBlWUOAnwOLMc39DmFK3f7Mtu2yDtsGDEB86+KBe4FLgRRMk8C3gXts287vuL1vn7HAdkxAkdlV/odlWbcAlwETgETABRQBK4E/27b9ac/fdafnUADSS5qavbyyPp+/fpTdZZ8RgJFJkcwdnsAZwxOYnZVAXMQRA10iIiIip5UBHYCInwKQ3tfs8fLRrsOsyillzb4yth2sai3nG4hlwdjk6NaAZFZWPFGhrhN4xSIiIiJ9r7cCEGXZinQQ5HRw7rjBnDtuMAA1jc2szyvn831lrM4pY+OBCpo8rfUPsG1TTWv7oSoe/SwXp8NiYloMZwxPYO7wBGZkxBMWrBK/IiIiIqAARKRbkSFBnDVqEGeNGgRAfZOH9fvLWZFdworsUjbnV7YbIfF4bTYeqGDjgQr++lE2QQ6L4YMiGZMSxZjkaMamRDE2JZqkqBB1ZBcREZEBRwGIyFEKC3Yyb0Qi80YkAmaE5PPcMlbmlLIiu4RtB6toO7Ox2Wuzq6iaXUXVvM7B1uUxYS6So0NJiAwmMTKExMgQEiKDSYoKYc6wBIbEh5/otyYiIiLS5xSAiBynyJAgFo5JYuGYJAAq6ppYnVvGymwTkOwuqgm4X2W9m8p6tylLEMCsrHgum5rGBRNTiAlTTomIiIicHhSAiPSy2PBgzhufzHnjkwEzQrKrsJqdhVXsOFTFzkPV7Cyspqax667ra3LLWJNbxr1vbOPcsUlcNjWdMclROBwWTsvC4QCnZeF0WAQHOQgNcuJwaEqXiIiInNoUgIj0sciQIKZnxDE9I651mddrU1LTyOGaRkpqmiipbqSkxjx2FlazfG8JLWklTc1e3tpSyFtbCrs9V7DTQajLQajLSajLSXpcGF8cN5jFE5JJiQnrq7coIiIi0mMKQEROAofDIik6lKTowA0Ni6sb+PemQ7y6IZ+tBVU9Pm6Tx0uTx0tVgxld2V9Wx4rsUn727+1MHRrLBRNSWDwhWfklIiIictKoD0g/pz4gp789RdW8sqGAZTuLqW5oxmvbeLx267PHa9Pk8dLg9nZ/MJ9J6TF8ecYQLp2Sqp4lIiIi0iNqRCiAAhDxs22bxmYvDW4PDW4vNY3NrM4t5e0thazMKQ3YTDHM5eSSyalcPXsok9NjVBZYREREOqVGhCLSjmVZrbkfLUYkRXLt7AzKa5t4f3sRb209xPK9Jbg9Jhipd3t4fu0Bnl97gHEp0Xx5RjqjB0eRHBNKckwo4cH6J0JERER6l+4uRAaAuIhgrpw5hCtnDqGyzs2rG/J5Zs3+diWCtx+q4mf/3t5uv5gwFykxJlclNsxFdFgQMWEuokNdRIe5iA1zMSsrnoTIkBP9lkRERKSfUgAiMsDEhLu4aV4WN56Ryfr95Tyz+gD/2XyQxuYjc0haepXsLKzu9HihLgdXzRzK7QuGqdKWiIiIdEsBiMgAZVkW0zPimZ4Rzz0XjeP1TQV8vq+cwsp6DlU2UFTV0DpVqysNbi+PrdjH06vzuGJ6OncsGE5GQsQJeAciIiLSHykAERFiwl3cMDeTG+Zmti7zem1Ka5so9AUjVQ1uqhuaqap3U9Xgpqq+mS0FlWw/ZMoEuz02z645wPOfH+CSyanccfZwxiRHn6R3JCIiIqcqBSAiEpDDYTEoKoRBUSFMJCbgNrZt89Huw/xl6V7W5pUD4LXhtY0HeW3jQWZlxXPD3AzOG5+My+k4kZcvIiIipygFICJyzCzLYuHoJM4eNYjVuWX8ZdlePt1T0rp+TW4Za3LLSIoK4epZQ7l61lCSYwI3XxQREZGBQQGIiBw3y7KYMyyBOcMS2Higgoc/yeGdbYWtvUeKqxv504d7+POyvczMjGNIXDhpcWGkxYaRFhdGemw4KbGhPR4lqWlspr7JQ2JksHqXiIiI9DMKQESkV00ZEstfrp1GUVUDz6zez7Nr9lNc3QiAx2uzKqeMVZQdsZ/DgtTYMDISwhkaH0FGQjiZCeGEuJzkHq4l+3ANOb7nluNNTIvh+jkZXDIltV3/ExERETl1qRN6P6dO6HKqc3u8vLetiCdW7mN17pGBR2+ICXNx5Yx0rp2dQWaiKnCJiIj0BXVCF5F+weV0cOGkFC6clMKBsjr2FteQX1FPQXk9BRX1HKyoJ7+8jqKqxh4fM9g3VavJY3qXVNa7efjTXB7+NJf5IxMZFBVCY7OXpmYvjc1eGt0emjxeGt1e89zsaf25qdnL4OhQxqVGMyE1hvGp0YxPjVZzRRERkT6iAERETpgh8eEMiQ8PuK7B7SG/vI68UvPYX1ZHXmktDW4vmYnhDB8UybBBEQwfFElabBjVDc28uO4AT63az/6yutbjtE2C76ncklpyS2p5c/Oh1mXJ0aHMG5HIdxaNIjVWDRZFRER6i6Zg9XOagiUDnddr8/Huwzy5Ko9lu4rp7X/SIoKdfGfRaG48IxOnQwnvIiIycGkKlogIpl/JwjFJLByTxP7SOlbnlmJZFsFBDkJ8D//PztbnlmUOh8W+klq2Haxi28FKth6sYuehKhqbzfSu2iYPP//Pdl7dUMBvvjSRCWmBe6KIiIhIzygAEZHTxtCEcIYmBJ7i1ZXJQ2KZPCS29XWzx8vq3DLufWMbe4trANhSUMklf/6M/5qXxbe+OIqIEP3zKSIiciz0f1ARkQ6CnA7mjUjkrbvm89DH2Ty4bC9NzV68NjzyWS6vbSxg6tA4xiZHMTYlmjEp0WTEh+PQFC0REZFuKQAREelEcJCD//7CSC6anMqPXt3CiuxSAEpqmnh/exHvby9q3TbM5WTk4EjS48JIjQkjNTaM1NhQUmPDSIgMoa6xmaoGN1X1zVTWu6lqcFPb6CHU5SAiOIiIkCAiQpxEhAQRFRpEVmIEIUHqbSIiIqcfBSAiIt3ISozg6Vtn8+qGAn791g5KapqO2Kbe7WFzfiWb8yt75ZwxYS6WTEnly9OHMCEtWh3fRUTktKEARESkByzL4kvT0rl4cip7imrYcaiKnYVV7DhUzY5DVZTWHhmUHI/KejdPrMzjiZV5jEmO4orp6Vw2NU39SUREpN9TACIichRcTgfjUqMZlxrdbnlxdQO5h2s5VNnQ2mDxUGUDByvqKattIjI0iOhQF9FhLmLCXESHmmlXjW4PNY0eahubqW1qpraxmYMVDRRWNbQee2dhNb98cwf3vb2TYYMiCHI4cDktnA6LIIcDp8NiSHwYF01KZd6IRJULFhGRU5oCEBGRXpAUFUpSVGivHMvrtVmdW8aLaw/w1tZDNLhNSeBmr83uopqA+6zMgRfW5jMoKoQlk1O5dGoa41M1dUtERE49akTYz6kRocjprbrBzZubD/HC2gOs319xVPuOTIrk0qlpLJ6QzPBBkX10hSIiMlD0ViNCBSD9nAIQkYGjsLKB8romPF6bZq+Nx+vF7bFpcHv4ZHcJb2w6SElNY8B9RyRFct74wZw3PpmJaTEaGRERkaOmTugiIgNMckwoyTGBp3mdPTqJ/3fBGJZnl/Lq+nze3VZEvdvTun5vcQ17i2v4y7JsUmNCWTQ+mSVTUpkyJFbBiIiInFAKQEREThNBTgcLRg1iwahB1DY28972Qt7eUsgnew635pEAHKxs4LEV+3hsxT6GDYrg8mnpXDo1jbTYsJN49SIiMlBoClY/pylYItKduqZmPtldwnvbCvlgRxFVDc0Bt5s7LIHLpqUxd1gC6XFhGhkREZF2NAVLRER6JDw4iMUTklk8IRm3x8vqnDJe31jAW1sOUdvkn6a1MqeUlTmm23tsuIsJqTGMT4tmYloME9NiGBofrqBERESOmwIQEZEBxOV0cObIRM4cmcjPl0zgve2FvLy+gM/2HMbbZkC8os7NZ3tL+GxvSeuyiWkx3H3eaM4amahAREREjpkCEBGRASos2MmSKWksmZJGUVUDr20o4OPdh9laUBlwmtaWgkpu/OcaZmfF873Fo5meEX8SrlpERPo7BSAiIsLg6FBuXzCc2xcMx7ZtDpTVs/VgJVsLKtlSUMma3DIam00i++rcMi7/20rOGZPE3YtGH9EVXkREpCsKQEREpB3LshiaEM7QhHAumJgCQFFVAw98uIfnPz9As2+u1tKdxSzdWcysrHgmp8cwwZcrkpkQgcOhKVoiIhKYAhAREenW4OhQfnXZRL561jD++MEeXttYQEsRxTW5ZazJLWvdNiokiHGp0UwdGsecYfHMzIwnIkT/uxEREUNlePs5leEVkZNhZ2EVv39vN+9vL+p22yCHxaT0GOYMS2Du8AQmpsUQHerSKImISD+jMrwiInLSjEmO5uEbZnCosp5NB/y5IlsLKimtbWq3bbPXZv3+Ctbvr+CvH2UD4LAgNjyY2DAXMeEuYsNcpMeF8+UZ6UxKjz0Zb0lERE4QBSAiInLMUmLCSIkJY/GEZABs2+ZQZQOb8ytZnVvKqpwydhyqOmI/rw1ltU2UdQhWnlyVx4yMOG45M4tF45NxapREROS00y8DEMuy0oGfA4uBBOAQ8BrwM9u2y4/iOPHAPcClQApQCrwD3GPbdn6A7fcBGZ0crsi27eROznMG8GNgDhAK7AX+CTxo27Yn0D4iIv2RZVmkxoaRGusPSsprm1qDkVU5peSX11PTGLgbO8DavHLW5pWTHhfGTWdkcuXMIUSHuk7UWxARkT7W73JALMsaDqwAkoDXgZ3ALGAhsAuYZ9t2aQ+Ok+A7zihgKfA5MAZYAhQDc23bzumwzz4gFvhjgEPW2Lb9fwHOswR4GWgAngfKgIuB0cBLtm1/uds33fX7UA6IiPQ7bo+Xyno3FXVNVNS5Ka1t4p2thfx708HWKlstgp0OYsJdRAQ7CQ8OIiLESVhwENGhQZw5IpHzJ6YQE6YARUSkr/VWDkh/DEDeBRYBd9m2/WCb5fcD3wIesm37jh4c5yHgq8AfbNv+dpvldwF/At61bXtxh332Adi2ndnDa43GjHbEYAKjtb7loZigZy5wtW3bz/XkeJ2cQwGIiJw2iqoaeHJlHk+vzqO8zt2jfYKDHJw7NonLpqazYNQggoMcfXyVIiID04AMQCzLGgZkA/uA4bZte9usi8JMxbKAJNu2a7s4TgRwGPACKbZtV7dZ5/CdI9N3jpw26/bBUQUg/wU8Cjxh2/aNHdadA3wIfGLb9oKeHK+TcygAEZHTToPbw6sbCvjX8lx2F9X0eL+4cBcXTUrlokkpzMiMVw6JiEgvGqhVsM7xPb/XNvgAsG272rKs5ZjRkTmYm/vOzAXCfMepbrvCtm2vZVnvYUZHFgI5HfYNsSzrOmAoUAtsxgQRgXI5Wq73nQDrPgHqgDMsywqxbbuxi+sVERlQQl1Orp41lKtnDaW6wU1dk4faxmbqmjzm56ZmsotreG1jAVsL/Enu5XVunlyVx5Or8oiPCObcsUmcNz6ZeSMSCXU5T+I7EhGRFv0tABnte97dyfo9mABkFF0HID05Dr7jdJQMPNlhWa5lWTfbtv1xT89j23azZVm5wHhgGLCji+vFsqzOhjjGdLWfiEh/FxXqIipAEvrC0UncOn8Ye4qqeXVDAa9vPEhBRX3r+rLaJl5Ym88La/MJD3Zy9uhBJEWFUlXvprLDo9lrY2GS6C0L38+mytdVM4dw6dQ0BTAiIr2kvwUgMb7nyk7Wtyzvroj8sR7nX8CnwDagGhM4fAMzWvK2ZVlzbdve1AfXKyIinRg5OIrvLR7D3YtG8/m+Mt7YdJD3txdRXO0fWK5r8vDWlsKjPnZRVSMbD1Twu3d3ce3soVw/J4Ok6NDevHwRkQGnvwUg3WmZ7Hu8iS0Bj2Pb9s86bLcVuMOyrBrgO8BPgcuO9zyBdDbXzjcyMu0ozikiclpyOCxmD0tg9rAEfrFkAhvzK3h3WyHvbSsit6TTtMAeKatt4sGle/n7x9lcPDmV/5qXxYS0mO53FBGRI/S3AKRlxKCzf/WjO2zX18dp8XdMAHJWH59HRER6wOGwmDY0jmlD4/jB4jHsLa7h0z0leG2bmDCX/xHuIjrURXCQA9sGGxvffzQ1e3lnayGPrdjXOrXL7bF5ZX0Br6wvYMmUVH504ViSojQiIiJyNPpbALLL9xwoNwNgpO+5s9yO3j5Oi2Lfc0SA88zwnaddDodlWUFAFtDMkYnuIiLSSyzLYuTgKEYOjjrqfW87axg3z8vkve1FPPpZLuvy/L1uX994kKU7irn7vNFcNydDFbdERHqovxVLX+Z7XuQrl9vKV4Z3HlAPrOrmOKt8283z7df2OA5MInvb83Vnru+5YyCx1Pe8mCOdBYQDK1QBS0Tk1BXkdHDBxBRevvMMXvv6PBaPT25dV93YzL1vbOPSvyxn04GKk3iVIiL9R78KQGzbzgbew/To+HqH1T/DjEA80bYHiGVZYyzLalcpyrbtGkwlqwhM3kZb3/Ad/90OPUDGW5YV3/GaLMvKAP7se/lUh9UvASXAVZZlzWizTyjwS9/LvwV+tyIicqqZMiSWv18/nSdvmUVWon/Qe0tBJZf+dTk/enULH+8+zL6SWtwebxdHEhEZuPpVI0IAy7KGAyuAJOB1TPna2ZieHbuBM2zbLm2zvQ1g27bV4TgJvuOMwoxUrAHGAkswU6rO8AU8Ldv/FPgBZlQkF1MFazhwIRAKvAVcZtt2U4fzXIoJRBqA54Ay4BJMid6XgCvt4/hDUCNCEZGTo7HZw0Mf5/DnZXtpaj4y2HA6LFJjQ8lMiCAjIZyJaTFMz4hn+KAILKvz6VrNHi+HKhtIjAwhLFilf0Xk1DEgO6G3sCxrCPBzzNSmBEwH9NeAn9m2XdZh24ABiG9dPHAvcCmQApQCbwP32Lad32HbBcAdwFRML5AIoALYiBlNebKzQMKyrHnAjzBTtUKBvcA/gQc6aWDYYwpAREROrrzSWu59Yxsf7Trco+1jw11MGxrH9Iw4JqfHUlnvZk9xNXuKa9hbVENOSQ1uj02Yy8klk1O5evZQJqfHdBm0iIicCAM6ABE/BSAiIiefbdss3VnMe9uK2Fday/6yOg5VNvTa8celRHPN7KFcOjWNyJD+Vj9GRE4XvRWA6F8xERGR42RZFl8YO5gvjB3cuqzB7eFAWR15pXXsLq5mfV4F6/eXU1bb1MWRjJgwF5X17tbX2w9V8ePXtvLrt3ZwxvAEokJdhAc7iQgJIjzYSWRIEIOjQzlr5CBiwo/sGi8icipRACIiItIHQl3O1vK/544zgYlt2+SW1LIur5z1+8vZfqia+HAXIwdHMSIpkpFJkYxIiiQyJIj1+yt4ZvV+/rP5II2+HJO6Jg8f7Cju9JxOh8WcYfEsGpfMF8cNJjU27IS8VxGRo6EpWP2cpmCJiJzeKuvcvLIhn6dX72dvcc1R7TsxLYbFE5K5bk4GMWEaGRGR46MpWCIiIgNATLiLm+dlcdMZmWwtqKKgoo7aRg91Tc3UNnmoa2ymurGZDfsr2NihF8mWgkq2FFTy9Ko8fn/lFOYOTzhJ70JExE8BiIiISD9gWRYT02OYmB7T6TZFVQ28v72I97YXsTK7BLfHzHI4WNnANY+s4qtnDePbXxxFSFDvlPf1em2yD9eQGBlCXERwrxxTRE5/CkBEREROE4OjQ7luTgbXzcmgqsHNO1sK+fXbO6ioc2Pb8NDHOXy6u4Q/XTWFkYOjjukclfVuPtl9mGW7ivl412FKa5uICgni0ZtmMivriH69XbJtm/zyerYWVLL1YCUF5fUEmhgeEuTg+jmZXQZfItJ/KAekn1MOiIiIdKWoqoG7X9zEp3tKWpeFBDn44fljuPGMzB71Fzlc3cgr6/P5cGcx6/LK8XiPvHeICHbyxC2zmJ7RdRCyMruUj3YVs6Wgkq0FlVQ1NPfofUSGBPHcV+cwIU1BiMjJoj4gAigAERGR7nm9No+v3Mdv3t7Zrmv76MFR3DQvk0unpAXsup5fXsfDn+Tw3OcHWitxdSUyJIinbp3NlCGxR6yra2rmp29s44W1+QH27JmEiGBeuvMMshIjjvkYInLsFIAIoABERER6bldhNd98bgM7C6vbLY8Nd3HVzKHcMDeD1Ngwsg/X8LePsnltQwHNAUY7JqXHsHB0EueMSSIs2MnV/1hFqa+/SVRoEM/cOqfddKmtBZXc9dwGcg7XHnGsmDAXE9KimZAaw/CkSIKdjnbrG5s9/Pqtna19UdLjwnj5zjMYHB163L8PETk6CkAEUAAiIiJHp7HZw58+2MNjK/ZR1+Rpt87psJiYFsOm/Ao63h5MSIvmhjmZLByTxKCokHbrdhVWc/XDq1qbLMaEuXj61tmMT43mX8v3cd/bO2ny+EdQLpiYzMWTUpmQFkN6XFi308DW5ZVx7SOraXCbY4weHMULt89V00WRE0wBiAAKQERE5NhU1rt5ce0BHl+5jwNl9Z1uNzsrnq8vHMH8kYldBgrbD1ZxzSOrqKgzIxVx4S4mpMW0yz0JD3by8yUTuHxaWo9yT9patrOY255Y2zoiMyMjjidvmR1w6piI9A0FIAIoABERkePj8dos3VnMv5bnsiK7tHX5OWOS+NrZw5mR2fPKVlsLKrnm4VUBE8snpEXzwFVTGTYo8piv9dUN+Xzr+U3trvGh66fj6jBtS0T6hhoRioiIyHFzOiy+OG4wXxw3mF2F1azILmF2VgLjUqOP+lgT0mJ48pbZXPfIaqob/UHIbfOzuPu80cfdf+SyqemU1br5xX+2A7B0ZzF3PbuB+y6fpE7vIv2IAhAREREBYHRyFKOTj60/SIvJQ2J5/JZZ3P3iJhyWxY8vHMvZo5N66QrhljOzKKtt5C/LsgF4e2shGw9UcN/lk1gwalCvnUdE+o4CEBEREelV04bG8eG3Fxx1nkdP3b1oNDUNzTy+Mg+AQ5UN3PjPNVw9ayg/unAskSG6vRE5lWnSpIiIiPS6vgo+Wo79syUT+Nu104iPCG5d/uya/Sz+4yesyC7pYm8ROdn0FYGIiIj0S+dPTGFmVjw/fnUr72wrBCC/vJ5rHl7NmOQompq9NLg91Ls9NLi9NDZ7yEiI4PJpaXx5xhD1EhE5SVQFq59TFSwRERnobNvmjU0Huef1ba0NC7vjsEwVra/MHMrC0YMIUiUtkW6pCpaIiIgIZkrWkilpzBmWwA9f2cLSncXd7uO14YMdxXywo5ikqBCumjWUr509nFBX95W6Gps9FFY2kBYbpsBF5BgoABEREZHTwuDoUP5500z2l9ZR1eAm1OUk1OUgzOUk1OXEsuD97UU8u2Y/q3LKWvcrrm7kgQ/3sHxvCQ/fMKNdXklH2w5WcudT69lfVkeoy8H41Bgmpbc8YslKiMDh6Lv8F5HTgaZg9XOagiUiInL0cktqeWHtAV5cm09JTWPr8syEcP518yyyEiOO2Of1jQV8/+XNNLi9nR43KiSIKUNjmZ4Rx8zMeKYMiSVCVbnkNKFO6AIoABERETkebo+Xfy3P5Tdv76Tlligu3MXDN8xo7QLf7PHy23d28vCnua37OSwzjas7TofF2JQoZmTEMyMzjhkZ8STHKPld+iflgIiIiIgcJ5fTwVfPGs7Q+HC++dxGGpu9lNe5ueaR1dx/5WTmDU/kG8+uZ/ne0tZ9hg2K4B/XzyA6LIgt+ZVszq9kS0Elm/MrKKlpand8j9dma0EVWwuqeGzFPgDS48KYkRHHjMx4pmfE4XRYFFTUc6iigYMV9RysqKewqoGMhAi+dvZwhsSHn8hfiUif0whIP6cREBERkd6xYX85tz6+ltJafxCRGBncLqg4d+xg7v/KZKJDXUfsb9s2+eX1rMsrZ21eGWv3lbOrqJrjudUKDnJw2/wsvnb2CE3lkpNOU7AEUAAiIiLSm/aX1nHTY2vIOVx7xLr/OXckd50z8qiSzCvr3WzYX87afSYo2Xigosscks4Mjg7hB+ePYcnktB6fv8HtYenOYnIO17BkSppGUuS4KQARQAGIiIhIb6uoa+KrT65jTa6plBUZEsT9V05m0fjk4z622+Nl28Eq1u4zIyRbCipxOS1SY8NIiQkjLTaU1NgwIkOSq9UNAAAZT0lEQVSDePjTXDYdqGi3/9ShsXx/8RgmpsUEHBFxe7x8treEf288yLvbCqlt8gAQ5nLyvcWjuXFupqp0yTFTACKAAhAREZG+0Njs4aGPc8gvr+OrZw1nRFLkCb8Gr9fmlQ0F/PadnRyubjxifWJkMEPiw8mID2dofDiltU28vbWQstqmAEczZmTE8bsrJjFs0Il/P9L/KQARQAGIiIjI6a6msZm/LNvLo5/m0uQ5uulbwwZF4LAs9hbXtC4LCXLw7S+O4pYzswhyOmhwe9h0oIK1eeV8vq+M7QermJUVzz0XjyMpShW7xE8BiAAKQERERAaKvNJa/vThHjbsryC/vA63J/A9XHJ0KJdMSeWSyamMT43G7bH587K9/HXZXprb1A4enxpNqMvJlvzKgIFNTJiLn10yniVTUrGs45+21ezxsrOwmg37yymrdTMmJYqpQ2JJilaQ018oABFAAYiIiMhA5PHaHKqsZ39ZHftL69hfVofHtlk4OolZmfEB8zy2Hazkey9tZtvBqqM616Jxg/nlZROOajSktrGZkppGdhfVsH5/OevzytmcX0m923PEtmmxYUwZEmseQ2OZmBZDqMt5VNcoJ4YCEAEUgIiIiEjPuT1eHvo4mwc+3Ntu1GPYoAhmZcYzMzOeyNAgfvGf7eSX17eujw03oyGXTE6los7NgXIT9Bwoq+dAeR3FVQ2U1DRRUtNIaU1TwECjp4IcFuNSo5k6JJZpGXFMHRLHkPiwXhmFkeOjAEQABSAiIiJy9PYW1/D+9iKyEiOYmRlHQmRIu/U1jc3c9/YOnlq1v93y8GAndU3HHlykxoQyNSOOwVGhbC2oZHNBz8oSJ0YGMyEthgmpMUxIi2Z8agzpcQpKTjR1QhcRERGRYzIiKbLLyl6RIUH88tKJXDAhhe++tJmCCjMa0tPgIzjIwaDIEFJiQpniG8mYNjSO5Jj207jcHi+7CqvZeKCCjQcq2LC/nOwAPVhKapr4aNdhPtp1uHVZTJiL8anRnD8xhWtmDcXZB+WFK+qayCmpZVJaDEFOR68ff6DSCEg/pxEQERER6Us1jc385q0dPPf5ATxem/BgJ0PiwhkSH0Z6XDhD4sNJjQklMSqExMgQEiKDiQoJOubRico6NxvzK1ifV84GX1BS3dDc5T5Th8byv1dMYkRS1DGdsyOv1+ap1Xn89u2d1DZ5GBofzh0LhnP59DRCggZufoqmYAmgAEREREROjNrGZhrcHuIjgk/o1Cev1yanpJZtByvZdrCKbQcr2VpQRWW9u912wU4H3zx3JF89axiu4xit2Ftcww9e3szavPIj1g2ODuG2+cO4ZvZQwoMH3kQiBSACKAARERGRgce2bfLL63l5fT5/Wba3XUni8anR/O6KSYxPjaGy3s3GAxWsyzOVuDYdqMDptJg7LIGzRg3irFGDSIsNA6Cp2SToP7i0fYK+ZUHH2+W4cBc3z8vi5nmZRIW6Tsh7PhUoABFAAYiIiIgMbLsKq/neS5vYlF/ZuizIYZGZGEH24ZojgoeOhg+KYP7IQazKKWVnYXW7Y3zt7OHcPC+Ll9bl8/CnORR36EifHhfG366dzsT0mGO69o0HKnh29X7iIoK5Y8EwYsODj+k4J4oCEAEUgIiIiIg0e7w8+lku97+/m8bmo+sWH8jk9Bh+e8UkxiRHty5rcHt4aV0+f/84u12J4mCng59cNJbr5mT0eGramtwyHly6h0/3lLQuS4wM5icXjeOSyb3T+LEvKAARQAGIiIiISIucwzV8/+XNfL7P5G84HRbjUqKZNtRfiave7eGT3Yf5ePdh1uSWtQtYQl0O7l40mpvnZXVaVavZ4+WVDQX84t/bqW70J8dfPDmV33xpIpEhgXNDbNtm+d5SHli6hzW5ZZ2+h/kjE/nlpRPISIg4ll9Bn1IAIoACEBEREZG2vF6bFdmlOB0Wk4fEdJks3uD2sDq3jM/2HKbB7eW2+cMYmhDeo/PsK6nlzqfXs+OQv7P8sEER/O3a6YxOjqLB7WFvcQ27CqvZXVTNypxSNreZJgbgsGDxhGTW51VQWNXQujwkyMFdXxjJbfOHERx0ZEK9x2vjte3jSrY/FgpABFAAIiIiInKyNLg9/PSNbTz3+YHWZaEuB6kxYewrrcXbyW12kMPiS9PSuPPsEWQlRlDT2Mzv39vF4yv2tdsnNtyFy+mg2ePF7bFp8nhp9njx2nDBxGT+eu1xxQFHTY0IRUREREROolCXk/sun8TMzHh+9NoWGtxeGtxeckqObKYIJl/kKzOHcPuCYaTH+UdaIkOCuPfi8Vw2NY3/9+oWthaYUZWKOnfA4wDtKn/1NwpARERERESOw+XT05mQFsOdT68jx9fJ3bIgMyGCUYMjGT04ilHJUczOSmBQVEinx5mUHstrX5vH4yvz+MP7u6lpDNyA0ZQGVgAiIiIiIjJgjU6O4q275rMyp5TEiBBGJEUSFnz0XdODnA5uOTOL6+YMpbiqkeAgB0EOC1eQg2CnA5fT0WmCfH9xYjNXeollWemW9f/bu/fwy6q6juPvTwMCKgyXRC18AgmBMlNQEDAcvBCUXCzIsoAMLFIj1JIeUhmzDOsxrhYlKY+ggkreigQV5JoaICWJXMRREXB0IK4Dhnz7Y6+jx+Pv/G4zc37n/Ob9ep79rNlrr73OOr919p7zPXuvtfPuJLcneTjJiiQnJ9lijvVs2fZb0eq5vdW7zRRlt0pyVJKPJLklyeok9yS5IsmRSX7sb5lk2yQ1zXLumvwdJEmSND423nAJ++y4Nb+wzdJ5BR/9NtpgCU/Z8rE8cbON2erxG7HZxhuy8YZLJj74gAm8ApJke+AqYGvgY8BXgN2APwb2S7JXVa2aRT1btXqeBlwMnAvsBLwC+NUke1TVrX27HAr8A3AHcAnwDeCJwK8BZwL7Jzm0pr4e9l/AR6fIv37mdyxJkiQtHhMXgAB/Txd8HFNVp/Uyk/wd8Frgr4CjZ1HP2+iCj5Oq6nV99RwDnNJeZ7++8jcBBwL/VlWP9pU/HvgC8Ot0wcj5U7zWdVW1fDZvTpIkSVrMJuoWrCRPBfYFVgDvHNh8AvAAcFiSaZ/c0rYf1sqfMLD59Fb/L7fXA6CqLq6qT/QHHy3/TuCMtrpsDm9HkiRJWu9MVAACvKClF00RCNwHXAk8FnjuDPXsAWwCXNn266/nUeCitrrPLNvVmyNt6qkK4KeS/EGS41v6jFnWK0mSJC0qk3YL1o4tvWnI9pvprpA8DfjMGtZDq2daSTYADm+rnxxS7MVt6d/vs8ARVfWNmV6jlR/2pMGdZrO/JEmSNA4m7QrI0pbeM2R7L3/zEdUDcCLwdOCCqrpwYNuDwFuBXYEt2vJ8ukHsy4DPzHS7mCRJkrSYTNoVkJn05iVb0yezzKqeNmD99XQzcR02uL2qVgJvHsi+LMm+wBXA7sBRdIPepzXskfftysguM+0vSZIkjYNJuwLSuzKxdMj2zQbKrbN6kryaLnD4MrBPVd01w2v+QFU9Qjd1L8Des91PkiRJmnSTFoDc2NJhYzN2aOmwsR1rpZ4kx9LNlnU9XfBx5wyvN5XvtNRbsCRJkrTemLQA5JKW7jv45PEkmwJ7AauBz81Qz+daub3afv31/ATdQPb+1+vffhxwEnAdXfCxcq5vounN1HXrtKUkSZKkRWSiApCq+irdFLnbAq8e2PwWuqsJ762qB3qZSXZK8iMzRVXV/cDZrfzygXpe0+q/cOBJ6CR5E92g82uAF1bVd6drb5LdkzxmivwX0D00EeCc6eqQJEmSFpNJHIT+KuAq4NQkLwRuoBvMvQ/dLVN/PlD+hpZmIP94upmoXpfkmXRPM98ZOAhYyUCAk+QI4C+A7wOXA8ckg1WyoqrO6lt/O/Dzbcrd21reM/jh80zeVFVXzfSGJUmSpMVi4gKQqvpqkmfTBQP7Ab8C3AGcCrxltoPBq2pVkj3onoR+MPBLwCrgPcCbq+q2gV22a+kS4Ngh1V4KnNW3fjbwUuA5wP7AhsC3gQ8Cp1fV5bNpqyRJkrRYpGpNZ6zVQkpyzS677LLLNdcMe06hJEmStOZ23XVXrr322muHPR5itiZqDIgkSZKkyWYAIkmSJGlkvAVrwiVZtckmm2y58847L3RTJEmStIjdcMMNrF69+q6q2mpN6jEAmXBJvkb35PYV6+glelMYf2Ud1a/xYV+vP+zr9Yd9vf6wr9cfC9nX2wL3VtV2MxWcjgGIppXkGoA1HWyk8Wdfrz/s6/WHfb3+sK/XH4uhrx0DIkmSJGlkDEAkSZIkjYwBiCRJkqSRMQCRJEmSNDIGIJIkSZJGxlmwJEmSJI2MV0AkSZIkjYwBiCRJkqSRMQCRJEmSNDIGIJIkSZJGxgBEkiRJ0sgYgEiSJEkaGQMQSZIkSSNjAKIpJdkmybuT3J7k4SQrkpycZIuFbpvmJslWSY5K8pEktyRZneSeJFckOTLJlOeBJHsmuSDJXUkeTPLfSY5NsmTU70Hzl+SwJNWWo4aUeUmSz7bPxf1JPp/kiFG3VfOT5JeSnJ/kjna+viPJRUl+ZYqyHtcTKsmvtn69rZ3Hb03yoSR7DClvX4+pJIckOS3J5Unubefnc2bYZ879Oc7ndh9EqB+TZHvgKmBr4GPAV4DdgH2AG4G9qmrVwrVQc5HkaOAfgDuAS4BvAE8Efg1YCpwPHFp9J4MkB7X8h4DzgLuAA4AdgQ9X1aGjfA+anyRPAb4ELAEeD7yyqs4cKPMa4DRgFV1ffw84BNgGeEdV/clIG605SfJG4K3Ad4F/pTvOfxJ4FnBJVb2hr6zH9YRK8nbgDXTH6Ufp+vtngQOBDYDDq+qcvvL29RhLch3wi8D9wG3ATsD7qup3hpSfc3+O/bm9qlxcfmQBLgQK+KOB/L9r+WcsdBtd5tSfL6A7Uf3EQP6T6IKRAn69L38zYCXwMPDsvvyN6QLTAn5zod+Xy4z9HuDTwFeBv239dtRAmW3p/kNbBWzbl78FcEvbZ4+Ffi8uQ/v40NZHnwI2nWL7hn3/9rie0KWdq78P3AlsPbBtn9Z3t9rXk7O0ftuhnaeXtT45Z0jZOffnJJzbvQVLPyLJU4F9gRXAOwc2nwA8AByW5HEjbprmqaourqpPVNWjA/l3Ame01WV9mw4BngCcW1VX95V/CHhjW/3DdddirSXH0AWfr6A7bqfye8BGwOlVtaKXWVV3A29rq0evwzZqntqtk28HHgReXlX3DZapqv/rW/W4nlw/Q3fL/OeramX/hqq6BLiPrm977OsxV1WXVNXN1aKCGcynP8f+3G4AokEvaOlFU3xhvQ+4Engs8NxRN0zrRO8LyiN9eb3PwCenKH8Z3ReePZNstC4bpvlLsjNwInBKVV02TdHp+vrfB8povOwJbAdcANzdxgccl+SPh4wJ8LieXDfT3T6zW5Kf7N+QZG9gU7qrnT329eIyn/4c+3O7AYgG7djSm4Zsv7mlTxtBW7QOJdkAOLyt9p+khn4GquoR4Gt09xw/dZ02UPPS+vVsutvrjp+h+HR9fQfdlZNtkjx2rTZSa8NzWvpt4Fq68R8nAicDVyW5NEn/r+Ie1xOqqu4CjqMbu/flJP+U5K+TfBC4iO4WvD/o28W+Xlzm059jf243ANGgpS29Z8j2Xv7mI2iL1q0TgacDF1TVhX35fgYm25vpBiD/blWtnqHsbPt66ZDtWjhbt/RoYBPgRXS/hD+dbhzf3sCH+sp7XE+wqjqZbuKQDYBXAn9GNwbom8BZA7dm2deLy3z6c+zP7QYgmqu01OnTJliSY4DX081wdthcd2+pn4Exk2Q3uqse76iq/1gbVbbUvh4/vak3AxxSVZ+pqvur6n+Al9LNrPP8YVO0TsG+HmNJ3gB8GDgL2B54HLArcCvwviR/M5fqWmpfLw7z6c8F/wwYgGjQTFHxZgPlNGGSvBo4BfgysE+7vN/Pz8AE6rv16ibgTbPcbbZ9fe8aNE3rxt0tvbWq/qt/Q7vy1buquVtLPa4nVJJldBMOfLyqXldVt1bVg1V1LV2w+S3g9W0SGbCvF5v59OfYn9sNQDToxpYOG+OxQ0uHjRHRGEtyLHA6cD1d8HHnFMWGfgbal9zt6Aat37qu2ql5eTxdn+0MPNT38MGim8EO4F0t7+S2Pl1fP5nuV9bbqurBddx2zV2v7/53yPZegLLJQHmP68nzkpZeMrihHZtfoPs+96yWbV8vLvPpz7E/txuAaFDvBLfv4BOyk2wK7AWsBj436oZpzSQ5DjgJuI4u+Fg5pOjFLd1vim17082CdlVVPbz2W6k18DDwz0OWL7YyV7T13u1Z0/X1/gNlNF4uo/vSsUOSx0yx/ektXdFSj+vJ1Zvd6AlDtvfyv9dS+3pxmU9/jv+5fSEfQuIyngs+iHDRLXS35BRwNbDlDGU3A76DD7FaNAuwnKkfRLgdY/6wKpdp+/Wc1kd/OZD/YuBRuqsjm7c8j+sJXYDfaP1zJ/DTA9v2b329GtjKvp68hdk9iHBO/TkJ5/a0Bkk/kGR7ug/11sDHgBuA3eme3HkTsGdVrVq4FmoukhxBN3Dx+8BpTH3f74qqOqtvn4PpBjw+BJwL3AUcSDe134eB3yhPHhMjyXK627BeWVVnDmz7I+BUuv+ozqP7FfUQYBu6wex/MtrWaraSbE33bKafBS6nuxXnZ+jGBRTdAwo/1Ffe43oCtbsRLqSb6ew+4CN0wcjOdLdnBTi2qk7p28e+HmOtfw5uq08CfpnuFqrLW953+8+98+nPsT+3L3Tk5zKeC/AU4D3AHXQf2q/TDVye9tdzl/Fb+OGv39Mtn51iv71oDzmj+3XtS8BrgSUL/Z5c5v0ZOGrI9gOAS+m+3DwA/CdwxEK322VWfbsl3dXpr7Vz9Sq6H46eO6S8x/UELsCGwLF0tz/fS3f73Uq657/sa19P1jKL/5dXrI3+HOdzu1dAJEmSJI2Mg9AlSZIkjYwBiCRJkqSRMQCRJEmSNDIGIJIkSZJGxgBEkiRJ0sgYgEiSJEkaGQMQSZIkSSNjACJJkiRpZAxAJEmSJI2MAYgkSZKkkTEAkSRJkjQyBiCSJM1BkuVJKsmyhW6LJE0iAxBJ0ki1L+8zLcsWup2SpHVjg4VugCRpvfWWabatGFUjJEmjZQAiSVoQVbV8odsgSRo9b8GSJI21/jEXSY5I8sUkq5OsTPLuJE8ast8OSd6b5FtJvpfk9ra+w5DyS5IcneTKJPe017glyZnT7HNIki8keTDJXUnOTfLTU5R7apJ/avWtbmW/lOSMJFut2V9IkiaLV0AkSZPitcC+wHnAJ4HnAa8AliXZvaq+0yuY5DnAp4FNgY8DXwZ2An4bOCjJC6vq6r7yjwH+DXgR8E3g/cC9wLbAS4ErgJsH2vMq4MBW/6XA7sDLgF9M8syqerjV/WTgP4HNgAuA84GNge2Aw4DTgVVr/NeRpAlhACJJWhBJlg/Z9FBVnThF/v7A7lX1xb46TgKOBU4Ejmx5Ad5L94X/d6rqfX3lXwacC5yT5Oeq6tG2aTld8PEJ4NBe8ND22ajVNWg/4DlV9aW+su8Hfgs4CPhgyz4E2BI4tqpOGfgbPA54FElajxiASJIWyglD8u+hCygGnd0ffDTL6a6CvDzJq1rgsCfd1Y7/6A8+AKrqvCSvobt68jzgsiRL6K5mrAaO7g8+2j4PA9/hx53aH3w076ILQHbjhwFIz+rBCqrqgSnqlaRFzTEgkqQFUVUZsmw+ZJdLp6jjHuA6uluadm7Zu7T04iH19PKf1dKdgKXAf1fV7XN4C1dPkffNlm7Rl/dx4H7gnUnOT/L7SX6+XamRpPWOAYgkaVJ8e0j+nS1dOpDeMaR8L3/zgfRbc2zP/06R90hLl/QyqurrdFdE/oXuNq9/BK4Hvp7kmDm+piRNPAMQSdKkeOKQ/N4sWPcMpFPOjgU8eaBcL5D4sdmr1paquqGqXgZsBTwb+DO6/4NPSXLkunpdSRpHBiCSpEnx/MGMJEuBZwIPATe07N44kWVD6unlX9vSr9AFIc9I8lNro6HDVNUjVXVNVb2dbqwIwMHr8jUladwYgEiSJsVhSZ41kLec7parD/QNHr8SuBF4XpJD+gu39b2Bm+im1qWqvg/8PbAJcEab9ap/n8ckecJ8G51ktyRTXb3p5T0437olaRI5C5YkaUFMMw0vwEer6rqBvH8HrkzyQbpxHL2ZrFbQ3dIEQFVVkiOATwHnJfkY3VWOHemuNtwHHN43BS/AW+ie43EAcFOSf23lnkL37JE/Bc6a1xuFlwOvTnIpcAtwN7B9e62HgZPnWa8kTSQDEEnSQhk2DS90QcVgAHIS8BG65368jG5mqbOA46tqZX/Bqvp8exjhG+kGfh8AfBf4APDWqrpxoPz3kuwHHA0cDhwBBLi9veYVc397P/ABYCO66YF3obvS8i2655G8o6quX4O6JWnipKoWug2SJA3VrpScAOxTVZ9d2NZIktaUY0AkSZIkjYwBiCRJkqSRMQCRJEmSNDKOAZEkSZI0Ml4BkSRJkjQyBiCSJEmSRsYARJIkSdLIGIBIkiRJGhkDEEmSJEkjYwAiSZIkaWQMQCRJkiSNjAGIJEmSpJExAJEkSZI0MgYgkiRJkkbGAESSJEnSyBiASJIkSRoZAxBJkiRJI/P/Ak96R7UK+3oAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 277, "width": 400 } }, "output_type": "display_data" } ], "source": [ "for composer in [\"Bach\", \"Beethoven\", \"Chopin\", \"Grieg\", \"Mozart\"]:\n", " plot_history(composer, [10, 25, 50, 100])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing Componists" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "comp_mat_val = np.zeros((len(composers), len(composers)))\n", "comp_mat_acc = np.zeros((len(composers), len(composers)))\n", "\n", "for k in range(len(composers)):\n", " auto_nn = keras.models.load_model(\"auto_nn_\" + str(epochs + np.sum(add_epochs)) + \"_\" + composers[k] + \".h5\")\n", " for j in range(len(composers)):\n", " data = training_sets[composers[j]][int(np.ceil(0.85 * len(training_sets[composers[j]]))):len(training_sets[composers[j]])]\n", " evaluation = auto_nn.evaluate(data, data)\n", " comp_mat_val[k, j] = evaluation[0]\n", " comp_mat_acc[k, j] = evaluation[1]\n", " del evaluation\n", " del auto_nn" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "#save_object(comp_mat_val, \"componist_validation_result.pkl\")\n", "comp_mat_val = load_object(\"componist_validation_result.pkl\")\n", "#save_object(comp_mat_acc, \"componist_accuracy_result.pkl\")\n", "comp_mat_acc = load_object(\"componist_accuracy_result.pkl\")" ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [], "source": [ "def plot_comparison(data, title, file, labels = [\"1\", \"Bach\", \"Beethoven\", \"Chopin\", \"Grieg\", \"Mozart\"]):\n", " fig, ax = plt.subplots(1, 1)\n", " ax.set_title(title)\n", " ax.set_xticklabels(labels)\n", " ax.set_yticklabels(labels)\n", " im = ax.imshow(data)\n", " plt.savefig(file)" ] }, { "cell_type": "code", "execution_count": 177, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 263, "width": 294 } }, "output_type": "display_data" } ], "source": [ "plot_comparison(comp_mat_acc, \"Accuracy on validation sets\", \"comparison_accuracy.png\")" ] }, { "cell_type": "code", "execution_count": 180, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 263, "width": 294 } }, "output_type": "display_data" } ], "source": [ "plot_comparison(-comp_mat_val, \"Loss on validation sets\", \"comparison_loss.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generating Music" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "def images_to_notes(images, start_threshold = 0.15, end_threshold = 0.1): \n", " def single_image(img, offset, start_threshold, end_threshold):\n", " notes = []\n", " for i in range(96):\n", " pitch, octave = from_numerical_value(i)\n", " j = 0\n", " while j < 96:\n", " if img[i, j] > start_threshold:\n", " k_0 = j\n", " k = 0\n", " while j < 96 and img[i, j] > end_threshold:\n", " k += 1\n", " j += 1\n", " if k > 1:\n", " notes.append(Note(pitch, octave, k / 24, offset + k_0 / 24))\n", " j += 1\n", " return notes\n", " \n", " offset = 0\n", " song = []\n", " for m in range(np.shape(images)[0]):\n", " song += single_image(images[m, :, :], offset, start_threshold, end_threshold)\n", " offset += 4\n", " \n", " return song" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating the Zero Song" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def generate_piece(vec, auto_composer, composer, name, epochs):\n", " song = auto_composer([vec])\n", " song_notes = images_to_notes(song[0][0], start_threshold = 0.8 * np.max(song), end_threshold = 0.5 * np.max(song))\n", " song_stream = write_to_stream(song_notes)\n", " song_stream.write(\"midi\", composer + \"_\" + str(epochs) + \"_\" + name + \".MID\")\n", " del song, song_notes, song_stream" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "zero_vec = np.zeros((1, 512)) " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "generated_epochs = [epochs] + [e + epochs for e in np.cumsum(add_epochs)]\n", "\n", "comps = [\"grieg\", \"mozart\"]\n", "\n", "for composer in comps:\n", " for e in generated_epochs:\n", " auto_nn = keras.models.load_model(\"auto_nn_\" + str(e) + \"_\" + composer + \".h5\")\n", " auto_composer = create_composer(auto_nn)\n", " generate_piece(zero_vec, auto_composer, composer, \"zero\", e)\n", " del auto_nn\n", " del auto_composer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating a random song" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": true }, "outputs": [], "source": [ "code = np.reshape(np.array([np.log(k) for k in range(1, 129)]), (1, 128))" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "auto_nn = keras.models.load_model(\"auto_nn_50_chopin.h5\")\n", "auto_chopin = create_composer(auto_nn)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'the_log_song.MID'" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "song = auto_chopin([code])\n", "song_notes = images_to_notes(song[0][0], start_threshold = 0.5 * np.max(song), end_threshold = 0.01 * np.max(song))\n", "song_stream = write_to_stream(song_notes)\n", "song_stream.write(\"midi\", \"the_log_song.MID\")" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": true }, "outputs": [], "source": [ "composer = create_composer(model)\n", "encoder = create_encoder(model)" ] }, { "cell_type": "code", "execution_count": 332, "metadata": {}, "outputs": [], "source": [ "#code = encoder([training_set[100]])\n", "#code[0] += 0.5 * encoder([training_set[0]])[0]\n", "#code[0] /= 1.5\n", "#code[0] += np.random.normal(0, 0.01, (1, 128))" ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'long_bach_log.MID'" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "song = composer(code)\n", "song_notes = images_to_notes(song[0][0], start_threshold = 0.4 * np.max(song), end_threshold = 0.1 * np.max(song))\n", "song_stream = write_to_stream(song_notes)\n", "song_stream.write(\"midi\", \"long_bach_log.MID\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A long trained Autoencoder" ] }, { "cell_type": "code", "execution_count": 267, "metadata": { "collapsed": true }, "outputs": [], "source": [ "notes_subset = note_collection[0:min(20, len(note_collection))]" ] }, { "cell_type": "code", "execution_count": 268, "metadata": { "collapsed": true }, "outputs": [], "source": [ "image_collection = convert_notes_to_images(note_collection)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "training_set = create_training_set(image_collection, shape)" ] }, { "cell_type": "code", "execution_count": 274, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_2 (InputLayer) (None, 10, 96, 96) 0 \n", "_________________________________________________________________\n", "reshape_4 (Reshape) (None, 10, 9216) 0 \n", "_________________________________________________________________\n", "time_distributed_7 (TimeDist (None, 10, 2048) 18876416 \n", "_________________________________________________________________\n", "time_distributed_8 (TimeDist (None, 10, 1024) 2098176 \n", "_________________________________________________________________\n", "flatten_2 (Flatten) (None, 10240) 0 \n", "_________________________________________________________________\n", "dense_13 (Dense) (None, 2048) 20973568 \n", "_________________________________________________________________\n", "dense_14 (Dense) (None, 512) 1049088 \n", "_________________________________________________________________\n", "encoder (BatchNormalization) (None, 512) 2048 \n", "_________________________________________________________________\n", "dropout_13 (Dropout) (None, 512) 0 \n", "_________________________________________________________________\n", "composer (Dense) (None, 2048) 1050624 \n", "_________________________________________________________________\n", "batch_normalization_4 (Batch (None, 2048) 8192 \n", "_________________________________________________________________\n", "activation_5 (Activation) (None, 2048) 0 \n", "_________________________________________________________________\n", "dropout_14 (Dropout) (None, 2048) 0 \n", "_________________________________________________________________\n", "dense_15 (Dense) (None, 2560) 5245440 \n", "_________________________________________________________________\n", "reshape_5 (Reshape) (None, 10, 256) 0 \n", "_________________________________________________________________\n", "time_distributed_9 (TimeDist (None, 10, 256) 1024 \n", "_________________________________________________________________\n", "activation_6 (Activation) (None, 10, 256) 0 \n", "_________________________________________________________________\n", "dropout_15 (Dropout) (None, 10, 256) 0 \n", "_________________________________________________________________\n", "time_distributed_10 (TimeDis (None, 10, 2048) 526336 \n", "_________________________________________________________________\n", "time_distributed_11 (TimeDis (None, 10, 2048) 8192 \n", "_________________________________________________________________\n", "activation_7 (Activation) (None, 10, 2048) 0 \n", "_________________________________________________________________\n", "dropout_16 (Dropout) (None, 10, 2048) 0 \n", "_________________________________________________________________\n", "time_distributed_12 (TimeDis (None, 10, 9216) 18883584 \n", "_________________________________________________________________\n", "reshape_6 (Reshape) (None, 10, 96, 96) 0 \n", "=================================================================\n", "Total params: 68,722,688\n", "Trainable params: 68,712,960\n", "Non-trainable params: 9,728\n", "_________________________________________________________________\n" ] } ], "source": [ "autoencoder = create_model(shape, momentum, length)" ] }, { "cell_type": "code", "execution_count": 292, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 412 samples, validate on 46 samples\n", "Epoch 1/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0147 - acc: 0.9947 - val_loss: 0.1407 - val_acc: 0.9663\n", "Epoch 2/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0146 - acc: 0.9948 - val_loss: 0.1401 - val_acc: 0.9660\n", "Epoch 3/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0144 - acc: 0.9949 - val_loss: 0.1385 - val_acc: 0.9662\n", "Epoch 4/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0145 - acc: 0.9949 - val_loss: 0.1380 - val_acc: 0.9665\n", "Epoch 5/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0147 - acc: 0.9948 - val_loss: 0.1391 - val_acc: 0.9664\n", "Epoch 6/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0145 - acc: 0.9948 - val_loss: 0.1385 - val_acc: 0.9662\n", "Epoch 7/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0142 - acc: 0.9950 - val_loss: 0.1393 - val_acc: 0.9659\n", "Epoch 8/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0145 - acc: 0.9949 - val_loss: 0.1387 - val_acc: 0.9664\n", "Epoch 9/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0144 - acc: 0.9949 - val_loss: 0.1415 - val_acc: 0.9666\n", "Epoch 10/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0145 - acc: 0.9949 - val_loss: 0.1482 - val_acc: 0.9669\n", "Epoch 11/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0146 - acc: 0.9948 - val_loss: 0.1494 - val_acc: 0.9668\n", "Epoch 12/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0143 - acc: 0.9949 - val_loss: 0.1449 - val_acc: 0.9669\n", "Epoch 13/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0148 - acc: 0.9947 - val_loss: 0.1491 - val_acc: 0.9667\n", "Epoch 14/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0147 - acc: 0.9947 - val_loss: 0.1406 - val_acc: 0.9663\n", "Epoch 15/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0145 - acc: 0.9949 - val_loss: 0.1397 - val_acc: 0.9665\n", "Epoch 16/100\n", "412/412 [==============================] - 18s 42ms/step - loss: 0.0148 - acc: 0.9948 - val_loss: 0.1506 - val_acc: 0.9667\n", "Epoch 17/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0148 - acc: 0.9947 - val_loss: 0.1527 - val_acc: 0.9663\n", "Epoch 18/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0144 - acc: 0.9949 - val_loss: 0.1552 - val_acc: 0.9661\n", "Epoch 19/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0148 - acc: 0.9948 - val_loss: 0.1543 - val_acc: 0.9665\n", "Epoch 20/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0144 - acc: 0.9949 - val_loss: 0.1545 - val_acc: 0.9660\n", "Epoch 21/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0146 - acc: 0.9948 - val_loss: 0.1522 - val_acc: 0.9667\n", "Epoch 22/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0146 - acc: 0.9948 - val_loss: 0.1511 - val_acc: 0.9665\n", "Epoch 23/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0151 - acc: 0.9946 - val_loss: 0.1527 - val_acc: 0.9662\n", "Epoch 24/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0147 - acc: 0.9948 - val_loss: 0.1546 - val_acc: 0.9660\n", "Epoch 25/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0149 - acc: 0.9946 - val_loss: 0.1511 - val_acc: 0.9668\n", "Epoch 26/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0144 - acc: 0.9949 - val_loss: 0.1505 - val_acc: 0.9666\n", "Epoch 27/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0140 - acc: 0.9950 - val_loss: 0.1542 - val_acc: 0.9667\n", "Epoch 28/100\n", "412/412 [==============================] - 19s 45ms/step - loss: 0.0140 - acc: 0.9950 - val_loss: 0.1540 - val_acc: 0.9667\n", "Epoch 29/100\n", "412/412 [==============================] - 18s 45ms/step - loss: 0.0142 - acc: 0.9950 - val_loss: 0.1531 - val_acc: 0.9662\n", "Epoch 30/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0135 - acc: 0.9952 - val_loss: 0.1511 - val_acc: 0.9667\n", "Epoch 31/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0139 - acc: 0.9951 - val_loss: 0.1526 - val_acc: 0.9664\n", "Epoch 32/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0137 - acc: 0.9952 - val_loss: 0.1513 - val_acc: 0.9663\n", "Epoch 33/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0141 - acc: 0.9950 - val_loss: 0.1532 - val_acc: 0.9664\n", "Epoch 34/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0136 - acc: 0.9951 - val_loss: 0.1551 - val_acc: 0.9666\n", "Epoch 35/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0136 - acc: 0.9952 - val_loss: 0.1532 - val_acc: 0.9666\n", "Epoch 36/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0136 - acc: 0.9952 - val_loss: 0.1548 - val_acc: 0.9658\n", "Epoch 37/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0142 - acc: 0.9949 - val_loss: 0.1548 - val_acc: 0.9659\n", "Epoch 38/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0135 - acc: 0.9952 - val_loss: 0.1516 - val_acc: 0.9663\n", "Epoch 39/100\n", "412/412 [==============================] - 18s 42ms/step - loss: 0.0137 - acc: 0.9951 - val_loss: 0.1528 - val_acc: 0.9665\n", "Epoch 40/100\n", "412/412 [==============================] - 18s 44ms/step - loss: 0.0134 - acc: 0.9953 - val_loss: 0.1537 - val_acc: 0.9660\n", "Epoch 41/100\n", "412/412 [==============================] - 19s 45ms/step - loss: 0.0137 - acc: 0.9951 - val_loss: 0.1535 - val_acc: 0.9663\n", "Epoch 42/100\n", "412/412 [==============================] - 19s 46ms/step - loss: 0.0136 - acc: 0.9952 - val_loss: 0.1526 - val_acc: 0.9662\n", "Epoch 43/100\n", "412/412 [==============================] - 19s 47ms/step - loss: 0.0139 - acc: 0.9951 - val_loss: 0.1477 - val_acc: 0.9656\n", "Epoch 44/100\n", "412/412 [==============================] - 19s 47ms/step - loss: 0.0137 - acc: 0.9952 - val_loss: 0.1414 - val_acc: 0.9650\n", "Epoch 45/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0141 - acc: 0.9950 - val_loss: 0.1424 - val_acc: 0.9657\n", "Epoch 46/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0135 - acc: 0.9952 - val_loss: 0.1433 - val_acc: 0.9661\n", "Epoch 47/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0138 - acc: 0.9951 - val_loss: 0.1437 - val_acc: 0.9654\n", "Epoch 48/100\n", "412/412 [==============================] - 19s 47ms/step - loss: 0.0135 - acc: 0.9952 - val_loss: 0.1437 - val_acc: 0.9658\n", "Epoch 49/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0133 - acc: 0.9953 - val_loss: 0.1428 - val_acc: 0.9656\n", "Epoch 50/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0132 - acc: 0.9954 - val_loss: 0.1416 - val_acc: 0.9660\n", "Epoch 51/100\n", "412/412 [==============================] - 19s 47ms/step - loss: 0.0133 - acc: 0.9953 - val_loss: 0.1489 - val_acc: 0.9661\n", "Epoch 52/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0137 - acc: 0.9951 - val_loss: 0.1431 - val_acc: 0.9660\n", "Epoch 53/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0134 - acc: 0.9952 - val_loss: 0.1429 - val_acc: 0.9659\n", "Epoch 54/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0130 - acc: 0.9954 - val_loss: 0.1512 - val_acc: 0.9662\n", "Epoch 55/100\n", "412/412 [==============================] - 19s 47ms/step - loss: 0.0129 - acc: 0.9955 - val_loss: 0.1513 - val_acc: 0.9664\n", "Epoch 56/100\n", "412/412 [==============================] - 19s 46ms/step - loss: 0.0128 - acc: 0.9955 - val_loss: 0.1487 - val_acc: 0.9661\n", "Epoch 57/100\n", "412/412 [==============================] - 20s 49ms/step - loss: 0.0133 - acc: 0.9953 - val_loss: 0.1403 - val_acc: 0.9656\n", "Epoch 58/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0130 - acc: 0.9955 - val_loss: 0.1434 - val_acc: 0.9656\n", "Epoch 59/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0136 - acc: 0.9952 - val_loss: 0.1467 - val_acc: 0.9660\n", "Epoch 60/100\n", "412/412 [==============================] - 20s 49ms/step - loss: 0.0135 - acc: 0.9952 - val_loss: 0.1586 - val_acc: 0.9659\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 61/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0133 - acc: 0.9953 - val_loss: 0.1554 - val_acc: 0.9663\n", "Epoch 62/100\n", "412/412 [==============================] - 19s 47ms/step - loss: 0.0134 - acc: 0.9953 - val_loss: 0.1582 - val_acc: 0.9664\n", "Epoch 63/100\n", "412/412 [==============================] - 19s 46ms/step - loss: 0.0136 - acc: 0.9952 - val_loss: 0.1527 - val_acc: 0.9659\n", "Epoch 64/100\n", "412/412 [==============================] - 19s 47ms/step - loss: 0.0142 - acc: 0.9950 - val_loss: 0.1434 - val_acc: 0.9652\n", "Epoch 65/100\n", "412/412 [==============================] - 20s 50ms/step - loss: 0.0149 - acc: 0.9947 - val_loss: 0.1423 - val_acc: 0.9653\n", "Epoch 66/100\n", "412/412 [==============================] - 19s 46ms/step - loss: 0.0146 - acc: 0.9948 - val_loss: 0.1459 - val_acc: 0.9659\n", "Epoch 67/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0143 - acc: 0.9949 - val_loss: 0.1497 - val_acc: 0.9653\n", "Epoch 68/100\n", "412/412 [==============================] - 20s 49ms/step - loss: 0.0145 - acc: 0.9949 - val_loss: 0.1458 - val_acc: 0.9655\n", "Epoch 69/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0137 - acc: 0.9952 - val_loss: 0.1445 - val_acc: 0.9657\n", "Epoch 70/100\n", "412/412 [==============================] - 20s 49ms/step - loss: 0.0133 - acc: 0.9953 - val_loss: 0.1442 - val_acc: 0.9659\n", "Epoch 71/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0133 - acc: 0.9953 - val_loss: 0.1432 - val_acc: 0.9659\n", "Epoch 72/100\n", "412/412 [==============================] - 19s 46ms/step - loss: 0.0136 - acc: 0.9952 - val_loss: 0.1438 - val_acc: 0.9653\n", "Epoch 73/100\n", "412/412 [==============================] - 20s 47ms/step - loss: 0.0139 - acc: 0.9951 - val_loss: 0.1428 - val_acc: 0.9661\n", "Epoch 74/100\n", "412/412 [==============================] - 20s 48ms/step - loss: 0.0134 - acc: 0.9953 - val_loss: 0.1437 - val_acc: 0.9659\n", "Epoch 75/100\n", "412/412 [==============================] - 19s 46ms/step - loss: 0.0130 - acc: 0.9954 - val_loss: 0.1424 - val_acc: 0.9659\n", "Epoch 76/100\n", "412/412 [==============================] - 19s 45ms/step - loss: 0.0128 - acc: 0.9955 - val_loss: 0.1418 - val_acc: 0.9659\n", "Epoch 77/100\n", "412/412 [==============================] - 19s 45ms/step - loss: 0.0129 - acc: 0.9955 - val_loss: 0.1423 - val_acc: 0.9658\n", "Epoch 78/100\n", "412/412 [==============================] - 19s 45ms/step - loss: 0.0129 - acc: 0.9954 - val_loss: 0.1433 - val_acc: 0.9655\n", "Epoch 79/100\n", "412/412 [==============================] - 19s 45ms/step - loss: 0.0130 - acc: 0.9954 - val_loss: 0.1465 - val_acc: 0.9654\n", "Epoch 80/100\n", "412/412 [==============================] - 19s 46ms/step - loss: 0.0127 - acc: 0.9955 - val_loss: 0.1445 - val_acc: 0.9654\n", "Epoch 81/100\n", "412/412 [==============================] - 19s 46ms/step - loss: 0.0125 - acc: 0.9956 - val_loss: 0.1418 - val_acc: 0.9655\n", "Epoch 82/100\n", "412/412 [==============================] - 19s 45ms/step - loss: 0.0124 - acc: 0.9957 - val_loss: 0.1414 - val_acc: 0.9655\n", "Epoch 83/100\n", "412/412 [==============================] - 19s 46ms/step - loss: 0.0127 - acc: 0.9956 - val_loss: 0.1425 - val_acc: 0.9656\n", "Epoch 84/100\n", "412/412 [==============================] - 19s 46ms/step - loss: 0.0130 - acc: 0.9954 - val_loss: 0.1447 - val_acc: 0.9653\n", "Epoch 85/100\n", "412/412 [==============================] - 19s 47ms/step - loss: 0.0131 - acc: 0.9954 - val_loss: 0.1447 - val_acc: 0.9659\n", "Epoch 86/100\n", "412/412 [==============================] - 19s 46ms/step - loss: 0.0131 - acc: 0.9954 - val_loss: 0.1451 - val_acc: 0.9654\n", "Epoch 87/100\n", "412/412 [==============================] - 19s 45ms/step - loss: 0.0129 - acc: 0.9955 - val_loss: 0.1450 - val_acc: 0.9649\n", "Epoch 88/100\n", "412/412 [==============================] - 18s 43ms/step - loss: 0.0134 - acc: 0.9953 - val_loss: 0.1445 - val_acc: 0.9655\n", "Epoch 89/100\n", "412/412 [==============================] - 19s 45ms/step - loss: 0.0135 - acc: 0.9952 - val_loss: 0.1465 - val_acc: 0.9648\n", "Epoch 90/100\n", "412/412 [==============================] - 19s 45ms/step - loss: 0.0137 - acc: 0.9951 - val_loss: 0.1471 - val_acc: 0.9656\n", "Epoch 91/100\n", "412/412 [==============================] - 18s 45ms/step - loss: 0.0135 - acc: 0.9952 - val_loss: 0.1467 - val_acc: 0.9653\n", "Epoch 92/100\n", "412/412 [==============================] - 18s 44ms/step - loss: 0.0136 - acc: 0.9952 - val_loss: 0.1466 - val_acc: 0.9654\n", "Epoch 93/100\n", "412/412 [==============================] - 18s 44ms/step - loss: 0.0131 - acc: 0.9954 - val_loss: 0.1431 - val_acc: 0.9653\n", "Epoch 94/100\n", "412/412 [==============================] - 18s 44ms/step - loss: 0.0127 - acc: 0.9955 - val_loss: 0.1425 - val_acc: 0.9654\n", "Epoch 95/100\n", "412/412 [==============================] - 18s 44ms/step - loss: 0.0126 - acc: 0.9956 - val_loss: 0.1426 - val_acc: 0.9657\n", "Epoch 96/100\n", "412/412 [==============================] - 19s 46ms/step - loss: 0.0128 - acc: 0.9955 - val_loss: 0.1409 - val_acc: 0.9651\n", "Epoch 97/100\n", "412/412 [==============================] - 19s 45ms/step - loss: 0.0127 - acc: 0.9955 - val_loss: 0.1438 - val_acc: 0.9657\n", "Epoch 98/100\n", "412/412 [==============================] - 18s 44ms/step - loss: 0.0133 - acc: 0.9953 - val_loss: 0.1471 - val_acc: 0.9654\n", "Epoch 99/100\n", "412/412 [==============================] - 18s 44ms/step - loss: 0.0130 - acc: 0.9954 - val_loss: 0.1475 - val_acc: 0.9652\n", "Epoch 100/100\n", "412/412 [==============================] - 18s 44ms/step - loss: 0.0126 - acc: 0.9955 - val_loss: 0.1459 - val_acc: 0.9655\n" ] } ], "source": [ "history = autoencoder.fit(training_set, training_set, validation_split = 0.1, batch_size = len(training_set) // 10, epochs = 100, verbose = True)" ] }, { "cell_type": "code", "execution_count": 293, "metadata": { "collapsed": true }, "outputs": [], "source": [ "autoencoder.save(\"beethoven_full_500.h5\")" ] }, { "cell_type": "code", "execution_count": 294, "metadata": { "collapsed": true }, "outputs": [], "source": [ "save_object(history, \"history_beethoven_full_500.pkl\")" ] }, { "cell_type": "code", "execution_count": 295, "metadata": { "collapsed": true }, "outputs": [], "source": [ "del history" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating a random song" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "autoencoder = keras.models.load_model(\"beethoven_full_500.h5\")" ] }, { "cell_type": "code", "execution_count": 327, "metadata": {}, "outputs": [], "source": [ "song_code = np.reshape(np.array([1 / np.sqrt(k) * np.sin(k/128) for k in range(1, 513)]), (1, 512))" ] }, { "cell_type": "code", "execution_count": 328, "metadata": { "collapsed": true }, "outputs": [], "source": [ "composer = create_composer(autoencoder)" ] }, { "cell_type": "code", "execution_count": 331, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'the_sin_song.MID'" ] }, "execution_count": 331, "metadata": {}, "output_type": "execute_result" } ], "source": [ "song = composer([song_code])\n", "song_notes = images_to_notes(song[0][0], start_threshold = 0.9 * np.max(song), end_threshold = 0.6 * np.max(song))\n", "song_stream = write_to_stream(song_notes)\n", "song_stream.write(\"midi\", \"the_sin_song.MID\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating a known song" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "encoder = create_encoder(autoencoder)\n", "composer = create_composer(autoencoder)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "encoded = encoder([training_set[0]])" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, 1, 512)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.shape(encoded)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "decoded = composer(encoded)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'beethoven_recreated.MID'" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "decoded_notes = images_to_notes(decoded[0][0], start_threshold = 0.9 * np.max(decoded), end_threshold = 0.8 * np.max(decoded))\n", "decoded_stream = write_to_stream(decoded_notes)\n", "decoded_stream.write(\"midi\", \"beethoven_recreated.MID\")" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "distorted = np.reshape(encoded + np.random.normal(0, 1.5, (1, 512)), (1, 512))" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "decoded = composer([distorted])" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'beethoven_distorted_3.MID'" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "decoded_notes = images_to_notes(decoded[0][0], start_threshold = 0.5 * np.max(decoded), end_threshold = 0.3 * np.max(decoded))\n", "decoded_stream = write_to_stream(decoded_notes)\n", "decoded_stream.write(\"midi\", \"beethoven_distorted_3.MID\")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "midi_elise = converter.parse(\"data/train_beethoven/elise.MID\")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "notes_elise = convert_midis_to_notes([midi_elise])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "images_elise = convert_notes_to_images(notes_elise)" ] }, { "cell_type": "code", "execution_count": 215, "metadata": {}, "outputs": [], "source": [ "first_elise = np.reshape(images_elise[0][20:30], (1, 10, 96, 96))" ] }, { "cell_type": "code", "execution_count": 216, "metadata": {}, "outputs": [], "source": [ "encoded_elise = encoder([first_elise])" ] }, { "cell_type": "code", "execution_count": 217, "metadata": {}, "outputs": [], "source": [ "decoded_elise = composer(encoded_elise)" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 213, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8mdy+J/6ut2dHkjTsUh1f3d+mpCr7m1m+11HSA1+SVqbLm9LliQVlj5U0V9Id9LgHAKB7XQe9uz+nZHS7hZL+vPk+M3u9ks546/Tiz+muVjJ63hlmdnhT2dmSPpve/HK39QIAAPUNgftRSUdK+rSZHSvpbiW97t+mZAS8s9x9rSS5+3ozO0tJ4N9iZsuUDIF7spKf3l2t5A8HAADQpVp+9OruzygJ+osk7SnpbCUD43xf0mvc/apM+WskHafka/9TJX1Y0nYlfzCc4ZVG/wcAAFkWMVPNbPl823nJ0bPftMNyRc+9qCNIViPbuarEQAhFg2mUGtzD6xl4p6BC+V1lO7NIGt511x3Wp1BRHTPPdezZZ/Nl+qnKLGdlDfI9VfS8Ar7HgengLr9RG7T23na/MCuL+egBAAiMoAcAIDCCHgCAwPo3ekK/mTpOsFG1lXZ4l51bt1Mw2UjRZAlVjK56unXFq0sM41+iTfaFvfMT8cx5Oj90wRPHtU6KkZsgRcqP/lLUTJx5Kfb8bEEbfZUJdCr2j8j2I8hN+iNpaGbBIBglJm3JDmo0vKhgEMgS/TNyfUEKHpPtVzG8sGBwjyoTvRTITrA0vOsu+UI1DVLV2Lg5ty47yJEVvD6eHcSnxPkxtmZNbl12YJky/WlKT2qzPTPQT8EEOtltVZqsR8oN/LP0p/lBhnad0TqB0LA6v+/GCq4R37GgdZ6yGco/r+Ga+sacsu/v5tZtPb5lPjWNzal2HTvyQusxm3X7z/OFsp8DZZ5XlQmfXhhWiZej86673wQAAJisCHoAAAIj6AEACIygBwAgsLgD5mjRkiPtdf3ZYUFnmqzCjjol9GsGvrZ6NbDMoM+7qs+rrnqX2X92X2UGw5kKA+ZUfB42kul8V3W6uOyuB/0eA9pgwBwAANARQQ8AQGAEPQAAgcUdMKefGp0Hx6mpObH/bcv9bN/tY3+A3ORABQOb2HC1v4Ozbb5D8+fny2QHwynSaD1phhYvyhdZ3zrYydBuBYPYlHgNfdOWjmVyExGV6JtSWvalL3pPZfu5NOp5zbxocKsSA+1UlT337OB982XGSrzvGiUGtnnw0dYVk62/BvqCK3oAAAIj6AEACIygBwAgMIIeAIDA6Iw31UTuTNPH51ZmkBTf3rFIKUWzo1XReGpV5zLZ2dt6qUQn1DqV6cBY12sm791z88xx8/se7Nm+smzGzN5tPDvj3rb8THmlBngqo+izosq2In+eNuGKHgCAwAh6AAACI+gBAAiMNnqg1+qaaKbEdoYXLcyXaZTYV3YAoYJJmMaeX915O5jUfHtBu3mvFJ2vvRoUS5o27e1VcEUPAEBgBD0AAIER9AAABEbQAwAQGJ3xqsh0KFl1ztG5IiMbWzuGNGbmO6EMb84OHpHf1S5fvbN1RdGMYXVNjVfXIBRFM39l6zidOs7U9VxLbGds7bp69lXC0OzZPdt2Y0t+Nj0baf248oJOhlbQibCTosGTKg0sU/Q+LHgv+Oj2jmXKKPNcc88tcGe42l6zbJGC2Q2HZs3KlCnxGVzh3NTWIamGw8oVPQAAgRH0AAAERtADABBY3DZ6G5Jl2lGspvapRmZyjZdddnfHxxS1J1bbeX8nEqnU7lZ1QpBetR8Ouj9AXQPmTDJF7ei9VGoiorq6q/RzYJmK75dKz7Wf512fz/F+vmZ9O/drOqG5ogcAIDCCHgCAwAh6AAACI+gBAAgsbmc8b8gzneZ61TWkTCchlBCgg1qhqM+r37KDRZUcoKajou0Efc16OchR9nNwaO7cfJkyx7VRYhCbzGe7JA3tNK91RcWBiJQZIGdsw4Zq26lDTachV/QAAARG0AMAEBhBDwBAYHHb6KOqa1CZoG2QCKzMYFFVB2uaJvo5yNHY+vV925fU3wmdphqu6AEACIygBwAgMIIeAIDACHoAAAIL2xnPhoY1PH9B68oZ9TzdsedX17KdSvrdiS7b+S9IJ77hxYtbVwwVdHKsOOPg2Jo1LbdtpKa32fBwblWZGRlLDVJSgm/LzA7W73MhM2COFbxmlWaJLBgwx5b815bbjVkFr2FmV1ZwPLYvmJlbN+fhZ1pub3j1S/P7z/YpLHhaVuK5zrr2xx3L9FUvP0/q6qicGWjHCt53R9/zQsvtX29d2HGzG0dndSyTNfInw9JDE35YDlf0AAAERtADABAYQQ8AQGAEPQAAgYXtjOfeUGNzZhSoTZ1nRSol0yloeHG+I4ZvycyuVHHWpn6OZFWkqCNKHQY941+2w1wv1fZcC7YTo2tkSZmR8dwLOl/V1fFw+c9bblft5jWjYF32VZyz8vFqG6+r81ldMp+LQ3PyM+VlP5NtuNpzKHxP1dWxLzO6oheMyHjHoXMya7blymQ7i3ojX6aT0bF6MosregAAAiPoAQAIjKAHACCwsG30cpdvn3ibSBUDHUCnx/rZlm4z8oOL1KFf5wH6LMjgTZVNtuefactubNzY8SEFYxVNDSVmUqznudXzGnNFDwBAYAQ9AACBEfQAAARG0AMAEFjczniy+mYNyyjVQS07mIVV/Jsq0+ljeJed82W2d65PdgazolnPxtavn1jd6tbHnjlDc+e27jo7M5sqzoQmleqoE4HNKpiNq+oxy/DR7fn9ZQdvKnpPFc1C2GlfBa/90G8f2Lqi4NTMzlbnJQew8UdWdNx/rzraFX5+1GRsdWYQqhKfeUUzEJZR9Bk877bdWm43vNpn7i6zWjsRPnnUC/lCVQYrGmDnSa7oAQAIjKAHACAwgh4AgMACt9G7fKxHbaVDvZnopQwG56lHY9Omvu0rKt+6tXOhOvfXz/Pjvgf7tq9+6u/nR0HHhmy/hqKJiSraeOyztWznyTKFJttgRR1wRQ8AQGAEPQAAgRH0AAAERtADABBY4M546l2HCZ8eA6L0XZVBKMqYYh1ngBDKvO9qfG9Wmv2yYJCu7EBrRZ1Ac2XGSgz2VTQgWHZQoWyZ7fV8JnJFDwBAYAQ9AACBEfQAAAQWu40eUwtt6QAq8u0FkwNV2U6JgZn6N3hTPZ+JXNEDABAYQQ8AQGAEPQAAgRH0AAAE1pOgN7MzzczTf+9rU+YtZnaLma0zsxfM7C4ze1fNFenNPwAApojag97M9pR0maQXdlDmQ5K+K+kQSd+UdLmkl0u6wsz+tu46AQAwXdUa9GZmkr4h6XlJX2lTZm9JfytptaTD3f2D7v5nkl4l6ReSzjWzo+usFwAA01XdV/RnSzpB0rslbWxT5j2SZkn6oruvHF/p7mskfS69+f6a6wUAwLRU24A5ZnawpAslXeLut5nZCW2Kjq+/ruC+f82U6U6fBmAZeelL8rvemhm8oWhCgxLG1q6r9LgpqY/9H2x4uGfb7t9gGnlDs2cXrCzx93yj9fz0gveOb93aeTs9fA2H5szpXCjzutpwwXNvtD63sfXru6lW12zWrPy6mo5jY8uW7IZr2W6hzDnzhw/+OldkzFtfj/nDm3NlhgsGiRlTa72/dsA+uTLZ4+jbCyajGWrdjjfy+8qVKXg/j+z9X1pXZD/vC/hoicnQMjlha0akGj5Oagl6MxuR9I+SHpf0qQ7FD0yXD2fvcPenzGyjpD3MbK67b+qw3+Vt7jqoQx0AAJgW6rqi/3NJh0r6XXfP/4nWamG6bHepuk7SvLTcDoMeAADsWNdBb2ZHKLmK/4K739l9lX7zHU3H793d/bA2dVouaUkNdQEAYErrqjNe01f2D0s6v+TDxq/kF7a5f0G6HGzDGQAAAXR7Rb+TpAPS/29p04HkcjO7XEknvXMkPSRp1/RxLd8AmNnLlHxt/2Sn9vnJZHTV04OuQgx9nL1ukB3meinX+arfevgaNjZNmY+ECSnq5Nizo9jH99j/O+jlfduXVK6zaJk+0WXKjK58vESNuudez+dUt0G/VdLX2ty3REm7/e1Kwn081G+SdIykE5UJekknNZUBAABd6iro04537Ya4vUBJ0P+Du3+16a5vSPq4pA+Z2TfGf0tvZov1Yo/9wsF2AADAxNT2O/qy3H2Fmf1PSZdKusfMrpS0TdJpkvZQfZ36AACY9voe9JLk7peZ2UpJH5P0TiWdAu+XdJ67/8Mg6gQAQEQ9C3p3v0DSBTu4/7tKJrYBAAA9wnz0AAAERtADABAYQQ8AQGAEPQAAgRH0AAAERtADABAYQQ8AQGADGTCnb4on2eleHyeGADAA2c8Oq/GaqDFW37aAEriiBwAgMIIeAIDACHoAAAIj6AEACCx2Z7x+6VWnP4mOf8AgZN93Tgc6TF1c0QMAEBhBDwBAYAQ9AACBxW6j71f7Nu3oAIBJiit6AAACI+gBAAiMoAcAIDCCHgCAwOJ2xjOTzZjZ/Xa8kV81Otr9doGpbmi4d9se9AxvdQ2CNck66i574o5KjxtT6/MYVv74/OG+x7fctpkzOm7XhvPnkJc4Zo0NG3Lrhl51UOu2t2wv2GGJ1zWz/7GHf9F5O5Psdc7iih4AgMAIegAAAiPoAQAILG4bvbt8+7ZB1wKIa9Dt6L00ydtcqzpjz6U93Hrr522/P38b9z3Yv51NsfODK3oAAAIj6AEACIygBwAgMIIeAIDACHoAAAIj6AEACIygBwAgMIIeAIDACHoAAAIj6AEACIygBwAgMIIeAIDACHoAAAIj6AEACIygBwAgMIIeAIDACHoAAAIj6AEACGxk0BXoKbPW2+6DqQcATAZDw73bdmOsd9suYWSfvVpXbNmaLzSUubZtNDpud/SpVV3UanLgih4AgMAIegAAAiPoAQAILHYbPW3yAPCiAbej99Loil8OugqTFlf0AAAERtADABAYQQ8AQGAEPQAAgRH0AAAERtADABAYQQ8AQGAEPQAAgRH0AAAERtADABAYQQ8AQGAEPQAAgRH0AAAERtADABAYQQ8AQGAEPQAAgRH0AAAERtADABAYQQ8AQGAEPQAAgRH0AAAERtADABAYQQ8AQGAEPQAAgRH0AAAERtADABBY10FvZruY2fvM7Dtm9qiZbTazdWZ2u5m918wK92FmS83sWjNbbWabzOw+MzvHzIa7rRMAAEiM1LCN0yV9WdJTkm6W9Likl0h6u6SvSjrJzE53dx9/gJmdIunbkrZIulLSaklvlXSRpGPSbQIAgC7VEfQPSzpZ0vfdvTG+0sw+JeluSacqCf1vp+sXSLpc0pik4939nnT9+ZJuknSamZ3h7stqqBsAANNa11/du/tN7v7d5pBP16+S9JX05vFNd50maTdJy8ZDPi2/RdJ56c0PdFsvAADQ+85429PlaNO6E9LldQXlb5O0SdJSM5vVy4oBADAd9CzozWxE0jvTm82hfmC6fDj7GHcflbRCSZPCvr2qGwAA00UdbfTtXCjpEEnXuvv1TesXpst1bR43vn5Rpx2Y2fI2dx1UqoYAAATXkyt6Mztb0rmSHpR05kQfni59h6UAAEBHtV/Rm9kHJV0i6X5Jv+fuqzNFxq/YF6rYgky5ttz9sDZ1WC5pSefaAgAQW61X9GZ2jqQvSvpPSa9Ne95nPZQuDyh4/IikfZR03nuszroBADAd1Rb0ZvYJJQPe/ERJyD/TpuhN6fLEgvuOlTRX0h3uvrWuugEAMF3VEvTpYDcXSlqu5Ov653ZQ/GpJz0k6w8wOb9rGbEmfTW9+uY56AQAw3XXdRm9m75L0V0pGuvuhpLPNLFtspbtfIUnuvt7MzlIS+LeY2TIlQ+CerOSnd1crGRYXAAB0qY7OePuky2FJ57Qpc6ukK8ZvuPs1ZnacpE8rGSJ3tqRHJX1U0qXN4+IDAIDqug56d79A0gUVHvcjSW/qdv8AAKA95qMHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACBLT0jeAAAJ60lEQVQwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAIbKBBb2Z7mNnXzezXZrbVzFaa2cVmtniQ9QIAIIqRQe3YzPaTdIek3SX9s6QHJR0h6SOSTjSzY9z9+UHVDwCACAZ5Rf/3SkL+bHf/fXf/pLufIOkiSQdK+usB1g0AgBAGEvRmtq+kN0haKelLmbv/QtJGSWea2bw+Vw0AgFAGdUV/Qrq8wd0bzXe4+wZJP5I0V9JR/a4YAACRDKqN/sB0+XCb+x9RcsV/gKQftNuImS1vc9dB1asGAEAcg7qiX5gu17W5f3z9oj7UBQCAsAbW674DS5e+o0Lufljhg5Mr/SV1VwoAgKlmUEE/fsW+sM39CzLlJmrvjdqgu/zGig8HAGCwNmqDJO3d7XYGFfQPpcsD2ty/f7ps14bfyfqGxrRBa2entx+suB2UN94vgmPdWxzn/uFY9w/HutjektZ3uxFz3+G34z2RDpbzqJKf1+3X3PPezOZLekpJ/4Hd3H1jF/tZLrX/ih/14Vj3B8e5fzjW/cOx7q2BdMZz919IukHJXysfzNz9l5LmSfq/3YQ8AAAYbGe8/6FkCNxLzez3JD0g6UhJr1Xylf2nB1g3AABCGNgQuOlV/eGSrlAS8OdK2k/SpZKOZpx7AAC6N9Cf17n7E5LePcg6AAAQGfPRAwAQ2EB63QMAgP7gih4AgMAIegAAAiPoAQAIjKAHACAwgh4AgMAIegAAAiPoAQAILGTQm9keZvZ1M/u1mW01s5VmdrGZLR503aYaM9vFzN5nZt8xs0fNbLOZrTOz283svWZWeA6Z2VIzu9bMVpvZJjO7z8zOMbPhfj+HqczMzjQzT/+9r02Zt5jZLenr8oKZ3WVm7+p3XaciM3uNmX3bzJ5KPyueMrMbzOxNBWU5pyswszenx/TJ9PPjMTO7ysyOblOe41yzcAPmpFPg3iFpd0n/rGR+4yOUTJbzkKRjGEe/PDN7v6QvK5k6+GZJj0t6iaS3S1oo6duSTvemE8nMTknXb5F0paTVkt4q6UBJV7v76f18DlOVme0p6WeShiXtJOksd/9qpsyHJF0m6Xklx3qbpNMk7SHpC+7+sb5Wegoxs/MkfUbSc5K+p+Qc31XSoZJudvePN5XlnK7AzD4v6eNKzs9rlBzrV0o6WckQ7O909282lec494K7h/on6XpJLunDmfV/l67/yqDrOJX+STpByRttKLP+pUpC3yWd2rR+gaRnJG2VdHjT+tlK/gBzSWcM+nlN9n+STNKNkn4h6X+nx+19mTJ7K/lAfF7S3k3rF0t6NH3M0YN+LpPxn6TT0+Pzb5LmF9w/o+n/nNPVjvFLJY1JWiVp98x9r02P22Mc597/C/XVvZntK+kNklZK+lLm7r+QtFHSmWY2r89Vm7Lc/SZ3/667NzLrV0n6Snrz+Ka7TpO0m6Rl7n5PU/ktks5Lb36gdzUO42wlf2S9W8l5W+Q9kmZJ+qK7rxxf6e5rJH0uvfn+HtZxSkqbmz4vaZOkP3L3Ddky7r696SbndDV7KWkevsvdn2m+w91vlrRByXEdx3HukVBBr+SDUZJuKAimDZJ+JGmupKP6XbGgxj8MR5vWjb8G1xWUv03Jh+tSM5vVy4pNZWZ2sKQLJV3i7rftoOiOjvW/ZsrgRUsl7SPpWklr0jbkT5jZR9q0G3NOV/OIkqakI8xs1+Y7zOxYSfOVfGs1juPcI9GC/sB0+XCb+x9Jlwf0oS6hmdmIpHemN5vfmG1fA3cflbRCSdvcvj2t4BSVHtd/VNIs8qkOxXd0rJ9S8k3AHmY2t9ZKTn2/ky6flnSvkvb5CyVdLOkOM7vVzJqvNDmnK3D31ZI+oaRPz/1m9n/M7H+Z2bck3aCk2eRPmx7Cce6RaEG/MF2ua3P/+PpFfahLdBdKOkTSte5+fdN6XoPu/LmSzmB/4u6bO5Qte6wXtrl/uto9Xb5f0hxJr1NydXmIkj4+x0q6qqk853RF7n6xko67I5LOkvRJJf0jnpB0ReYrfY5zj0QL+k4sXcb6qUGfmdnZks5V8ouGMyf68HTJa5BhZkcouYr/grvfWccm0yXHutX4z7RM0mnu/gN3f8Hdfy7pbZKelHRcu59/FeA4t2FmH5d0taQrJO0naZ6kwyQ9JumfzOxvJrK5dMlxnqBoQd/pCmZBphwmyMw+KOkSSfdLem369VwzXoMKmr6yf1jS+SUfVvZYr++iahGtSZePuftPm+9Iv0UZ/4bqiHTJOV2BmR2vpNPjv7j7R939MXff5O73KvmD6leSzk07UUsc556JFvQPpct2bfD7p8t2bfjYATM7R9IXJf2nkpBfVVCs7WuQhtk+SjrvPdarek5ROyk5ZgdL2tI0SI4r+cWIJF2errs4vb2jY/0yJVdPT7r7ph7XfaoZP25r29w//ofAnEx5zumJeUu6vDl7R3pO3q0kgw5NV3OceyRa0I+fUG/IjthmZvMlHSNps6R/73fFpjoz+4SkiyT9REnIP9Om6E3p8sSC+45V8quHO9x9a/21nNK2Svpam3//kZa5Pb09/rX+jo71SZkyeNFtSgJjfzObWXD/IelyZbrknK5mvHf8bm3uH1+/LV1ynHtl0D/kr/ufGDCnF8f0/PTY3SNp5w5lF0h6Vgx6Uefxv0DFA+bsIwbMqXpMv5ken89m1r9eUkPJ1f6idB3ndLVj/AfpsVkl6RWZ+05Kj/NmSbtwnHv7bzoMgfuApCOVjMT0sKSlzhC4paVjpl+hZISry1TcPrbS3a9oeszvK+mAs0XSMiXDWJ6sdBhLSX/g0U68HjKzC5R8fV80BO6HJV0qhsCdEDPbXcm4Gq+U9EMlXyPvpaTt2JUMpHNVU3nO6QlKv1W9XsmvGjZI+o6S0D9Yydf6Jukcd7+k6TEc514Y9F8avfgnaU9J31AydvU2Sb9U0oFsh1ej/Cs8lhco+eDb0b9bCh53jNIBSZT81f4zSX8maXjQz2mq/VObK/qm+98q6VYlH6YbJf1Y0rsGXe/J/k/Szkq+6VuRfk48r+Ti4Kg25TmnJ36MZ0g6R0lz6XolTSbPKBm74A0c5/78C3dFDwAAXhStMx4AAGhC0AMAEBhBDwBAYAQ9AACBEfQAAARG0AMAEBhBDwBAYAQ9AACBEfQAAARG0AMAEBhBDwBAYAQ9AACBEfQAAARG0AMAEBhBDwBAYAQ9AACBEfQAAAT2/wHMeMeSdPYntwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 250, "width": 253 } }, "output_type": "display_data" } ], "source": [ "plt.imshow(decoded_elise[0][0][0], origin = \"lower\")" ] }, { "cell_type": "code", "execution_count": 214, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'beethoven_elise_recreated.MID'" ] }, "execution_count": 214, "metadata": {}, "output_type": "execute_result" } ], "source": [ "decoded_notes_elise = images_to_notes(decoded_elise[0][0], start_threshold = 0.7 * np.max(decoded_elise), end_threshold = 0.6 * np.max(decoded_elise))\n", "decoded_stream_elise = write_to_stream(decoded_notes_elise)\n", "decoded_stream_elise.write(\"midi\", \"beethoven_elise_recreated.MID\")" ] }, { "cell_type": "code", "execution_count": 237, "metadata": {}, "outputs": [], "source": [ "distorted_elise_code = np.reshape((encoded_elise[0] * 1.3 + encoded[0] * 0.3 + 0.4 * np.random.normal(0, 1.0, (1, 512))) / 2, (1, 512))" ] }, { "cell_type": "code", "execution_count": 238, "metadata": {}, "outputs": [], "source": [ "distorted_elise = composer([distorted_elise_code])" ] }, { "cell_type": "code", "execution_count": 239, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'beethoven_elise_distorted_10.MID'" ] }, "execution_count": 239, "metadata": {}, "output_type": "execute_result" } ], "source": [ "distorted_notes_elise = images_to_notes(distorted_elise[0][0], start_threshold = 0.6 * np.max(distorted_elise), end_threshold = 0.01 * np.max(distorted_elise))\n", "distorted_stream_elise = write_to_stream(distorted_notes_elise)\n", "distorted_stream_elise.write(\"midi\", \"beethoven_elise_distorted.MID\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# A little LSTM to do the job?" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "scrolled": true }, "outputs": [], "source": [ "notes_embedded = [embed_song(to_notewise(song)) for song in note_collection[4:5]]" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4082, 288)\n" ] } ], "source": [ "training = [make_lstm_training(song) for song in notes_embedded]" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [], "source": [ "X = training[0][0]\n", "y = training[0][1]\n", "\n", "for k in range(1, len(training)):\n", " print(k)\n", " X = np.concatenate((X, training[k][0]), axis = 0)\n", " y = np.concatenate((y, training[k][1]), axis = 0)\n", " \n", "#X = load_object(\"lstm_training_X_20000.pkl\")\n", "#y = load_object(\"lstm_training_y.pkl\")" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "lstm_10 (LSTM) (None, 30, 500) 1578000 \n", "_________________________________________________________________\n", "dropout_10 (Dropout) (None, 30, 500) 0 \n", "_________________________________________________________________\n", "lstm_11 (LSTM) (None, 30, 400) 1441600 \n", "_________________________________________________________________\n", "dropout_11 (Dropout) (None, 30, 400) 0 \n", "_________________________________________________________________\n", "lstm_12 (LSTM) (None, 300) 841200 \n", "_________________________________________________________________\n", "dropout_12 (Dropout) (None, 300) 0 \n", "_________________________________________________________________\n", "dense_4 (Dense) (None, 288) 86688 \n", "=================================================================\n", "Total params: 3,947,488\n", "Trainable params: 3,947,488\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "lstm = create_lstm()" ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n", "4052/4052 [==============================] - 20s 5ms/step - loss: 5.2263 - acc: 0.0575\n", "Epoch 2/10\n", "4052/4052 [==============================] - 11s 3ms/step - loss: 4.5370 - acc: 0.0684\n", "Epoch 3/10\n", "4052/4052 [==============================] - 10s 3ms/step - loss: 4.4647 - acc: 0.0629\n", "Epoch 4/10\n", "4052/4052 [==============================] - 10s 3ms/step - loss: 4.4418 - acc: 0.0743\n", "Epoch 5/10\n", "4052/4052 [==============================] - 10s 3ms/step - loss: 4.4367 - acc: 0.0755\n", "Epoch 6/10\n", "4052/4052 [==============================] - 10s 3ms/step - loss: 4.4376 - acc: 0.0644\n", "Epoch 7/10\n", "4052/4052 [==============================] - 11s 3ms/step - loss: 4.4257 - acc: 0.0755\n", "Epoch 8/10\n", "4052/4052 [==============================] - 11s 3ms/step - loss: 4.4249 - acc: 0.0679\n", "Epoch 9/10\n", "4052/4052 [==============================] - 11s 3ms/step - loss: 4.4173 - acc: 0.0676\n", "Epoch 10/10\n", "4052/4052 [==============================] - 11s 3ms/step - loss: 4.4168 - acc: 0.0721\n" ] } ], "source": [ "history = lstm.fit(X, y, batch_size = 400, epochs = 10)\n", "#lstm = keras.models.load_model(\"lstm_beethoven_80.h5\")" ] }, { "cell_type": "code", "execution_count": 400, "metadata": { "collapsed": true }, "outputs": [], "source": [ "piece = note_collection[5:6]" ] }, { "cell_type": "code", "execution_count": 401, "metadata": { "collapsed": true }, "outputs": [], "source": [ "embedded = [embed_song(to_notewise(song)) for song in notes_subset]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "piece_input = [make_lstm_training(song) for song in embedded]" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "init = np.reshape(X[0], (1, 30, 288))" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [], "source": [ "current = init\n", "for k in range(270):\n", " input = np.reshape(current[0, k:(k + 30)], (1, 30, 288))\n", " vec = lstm.predict(input)\n", " idx = np.argmax(vec)\n", " pred = np.zeros((1, 288))\n", " pred[0, idx] = 1\n", " intermediate = np.concatenate((current[0], pred), axis = 0)\n", " current = np.reshape(intermediate, (1, 30 + k + 1, 288))" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [], "source": [ "song = inverse_embed(current[0])" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['w12', 's51', 'w12', 's51', 'e62', 'e53', 'e56', 'e51', 'w12', 's44', 's41', 'e51', 'w12', 's51', 'e44', 'e41', 'w12', 's51', 'e51', 'w12', 's61', 's52', 's55', 'e51', 'w12', 's51', 'w12', 's51', 'e61', 'e52', 'e55', 'w12', 's67', 's24', 'e60', 'e51', 'w12', 's39', 's53', 's32', 'e51', 'w12', 's67', 'e47', 'e44', 'e51', 'w12', 's51', 's42', 'e55', 'e20', 'w12', 's20', 's56', 's44', 's44', 'w12', 's67', 'e25', 'e55', 'e55', 'w12', 's27', 'e27', 'e36', 'e24', 'w12', 's39', 's24', 's29', 's44', 's36', 'e22', 's36', 's31', 'w12', 's46', 's29', 's29', 'e27', 'w12', 's60', 's42', 'e55', 'e27', 'w12', 's32', 's27', 'e44', 'e20', 'w12', 's32', 's17', 's44', 'e46', 's36', 's34', 'w12', 's20', 'e71', 's52', 's44', 'w12', 's65', 's55', 'w2', 'e28', 'w6', 's63', 's53', 's72', 'e27', 'w12', 's59', 's58', 'w12', 's61', 's55', 's53', 'e39', 'w60', 'e55', 'e36', 'w12', 's20', 'e47', 'e61', 'e67', 'w6', 's60', 's49', 's67', 's67', 'e55', 'e44', 'e55', 'e44', 'e36', 'e68', 'e24', 'w12', 's20', 'e32', 'e24', 'e27', 'w12', 's67', 's67', 's24', 'e55', 'e44', 'e44', 'w12', 's47', 's28', 'e56', 'w8', 's48', 's32', 's44', 'e51', 'e44', 'e17', 'w12', 's32', 's36', 's31', 's40', 'w12', 'e28', 's36', 's44', 'e54', 'e28', 'e27', 'w12', 's32', 's27', 's24', 'e20', 'w12', 's44', 'e71', 's29', 'e65', 'w12', 's60', 's42', 's29', 'e27', 'w12', 's46', 's20', 's53', 'e39', 's25', 'w12', 's61', 's53', 'e41', 'e27', 'w12', 's60', 's49', 's53', 'e20', 'w6', 's46', 's55', 's45', 'e39', 'w12', 's47', 's28', 'e56', 'e24', 'w6', 's54', 's20', 'e47', 'e20', 'w6', 's54', 's20', 's72', 'e28', 'w6', 's47', 's28', 'e52', 's39', 'w6', 's44', 's20', 'e47', 'e48', 'w6', 's47', 'e62', 'e52', 's39', 'w6', 's52', 's20', 'e47', 'e28', 'w6', 's39', 's28', 's60', 'e60', 'w6', 's46', 's20', 'e47', 'e28', 'w6', 's47', 'e62', 'e56', 'e20', 'w6', 's63', 's20', 'e39', 'e48', 'w36', 'e62', 's52', 'e60', 'e53', 'e39', 'w36', 'w36', 's48', 's56', 'e60', 'e39', 'w36', 's25', 'e36', 'e44', 'e27', 'w12', 's46', 's20', 's24', 'e48', 's25', 'e36', 's31', 'e27', 'w12', 'w95', 's60', 'e60', 'e48', 's25', 'e36', 'w36']\n" ] } ], "source": [ "print(song)" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [], "source": [ "notes = from_notewise(song)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [], "source": [ "s = write_to_stream(notes)" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'beethoven_lstm.MID'" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.write(\"midi\", \"beethoven_lstm.MID\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }