ディープラーニング-シーケンスモデルウーエンダレッスン5最初の週の課題3回答(LSTMネットワークでジャズソロを即興で演奏する)



Deep Learning Sequence Model Wu Enda Lesson 5 First Week Assignment 3 Answer

from __future__ import print_function import IPython import sys from music21 import * import numpy as np from grammar import * from qa import * from preprocess import * from music_utils import * from data_utils import * from keras.models import load_model, Model from keras.layers import Dense, Activation, Dropout, Input, LSTM, Reshape, Lambda, RepeatVector from keras.initializers import glorot_uniform from keras.utils import to_categorical from keras.optimizers import Adam from keras import backend as K



IPython.display.Audio('./data/30s_seq.mp3')



X, Y, n_values, indices_values = load_music_utils() print('shape of X:', X.shape) print('number of training examples:', X.shape[0]) print('Tx (length of sequence):', X.shape[1]) print('total # of unique values:', n_values) print('Shape of Y:', Y.shape) n_a = 64



reshapor = Reshape((1, 78)) # Used in Step 2.B of djmodel(), below LSTM_cell = LSTM(n_a, return_state = True) # Used in Step 2.C densor = Dense(n_values, activation='softmax') # Used in Step 2.D

# GRADED FUNCTION: djmodel def djmodel(Tx, n_a, n_values): ''' Implement the model Arguments: Tx -- length of the sequence in a corpus n_a -- the number of activations used in our model n_values -- number of unique values in the music data Returns: model -- a keras model with the ''' # Define the input of your model with a shape X = Input(shape=(Tx, n_values)) # Define s0, initial hidden state for the decoder LSTM a0 = Input(shape=(n_a,), name='a0') c0 = Input(shape=(n_a,), name='c0') a = a0 c = c0 ### START CODE HERE ### # Step 1: Create empty list to append the outputs while you iterate (≈1 line) outputs = [] # Step 2: Loop for t in range(Tx): # Step 2.A: select the 't'th time step vector from X. x = Lambda(lambda x: X[:,t,:])(X) # Step 2.B: Use reshapor to reshape x to be (1, n_values) (≈1 line) x = reshapor(x) # Step 2.C: Perform one step of the LSTM_cell a, _, c = LSTM_cell(x, initial_state=[a, c]) # Step 2.D: Apply densor to the hidden state output of LSTM_Cell out = densor(a) # Step 2.E: add the output to 'outputs' outputs.append(out) # Step 3: Create model instance model = Model(inputs=[X, a0, c0], outputs=outputs) ### END CODE HERE ### return model

model = djmodel(Tx = 30 , n_a = 64, n_values = 78) opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, decay=0.01) model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) m = 60 a0 = np.zeros((m, n_a)) c0 = np.zeros((m, n_a)) model.fit([X, a0, c0], list(Y), epochs=100) # GRADED FUNCTION: music_inference_model def music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 100): ''' Uses the trained 'LSTM_cell' and 'densor' from model() to generate a sequence of values. Arguments: LSTM_cell -- the trained 'LSTM_cell' from model(), Keras layer object densor -- the trained 'densor' from model(), Keras layer object n_values -- integer, umber of unique values n_a -- number of units in the LSTM_cell Ty -- integer, number of time steps to generate Returns: inference_model -- Keras model instance ''' # Define the input of your model with a shape x0 = Input(shape=(1, n_values)) # Define s0, initial hidden state for the decoder LSTM a0 = Input(shape=(n_a,), name='a0') c0 = Input(shape=(n_a,), name='c0') a = a0 c = c0 x = x0 ### START CODE HERE ### # Step 1: Create an empty list of 'outputs' to later store your predicted values (≈1 line) outputs = [] # Step 2: Loop over Ty and generate a value at every time step for t in range(Ty): # Step 2.A: Perform one step of LSTM_cell (≈1 line) a, _, c = LSTM_cell(x, initial_state=[a, c]) # Step 2.B: Apply Dense layer to the hidden state output of the LSTM_cell (≈1 line) out = densor(a) # Step 2.C: Append the prediction 'out' to 'outputs'. out.shape = (None, 78) (≈1 line) outputs.append(out) # Step 2.D: Select the next value according to 'out', and set 'x' to be the one-hot representation of the # selected value, which will be passed as the input to LSTM_cell on the next step. We have provided # the line of code you need to do this. x = Lambda(one_hot)(out) # Step 3: Create model instance with the correct 'inputs' and 'outputs' (≈1 line) inference_model = Model(inputs=[x0, a0, c0], outputs=outputs) ### END CODE HERE ### return inference_model

inference_model = music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 50) x_initializer = np.zeros((1, 1, 78)) a_initializer = np.zeros((1, n_a)) c_initializer = np.zeros((1, n_a)) # GRADED FUNCTION: predict_and_sample def predict_and_sample(inference_model, x_initializer = x_initializer, a_initializer = a_initializer, c_initializer = c_initializer): ''' Predicts the next value of values using the inference model. Arguments: inference_model -- Keras model instance for inference time x_initializer -- numpy array of shape (1, 1, 78), one-hot vector initializing the values generation a_initializer -- numpy array of shape (1, n_a), initializing the hidden state of the LSTM_cell c_initializer -- numpy array of shape (1, n_a), initializing the cell state of the LSTM_cel Returns: results -- numpy-array of shape (Ty, 78), matrix of one-hot vectors representing the values generated indices -- numpy-array of shape (Ty, 1), matrix of indices representing the values generated ''' ### START CODE HERE ### # Step 1: Use your inference model to predict an output sequence given x_initializer, a_initializer and c_initializer. pred = inference_model.predict([x_initializer, a_initializer, c_initializer]) # Step 2: Convert 'pred' into an np.array() of indices with the maximum probabilities indices = np.argmax(pred, axis=-1) # Step 3: Convert indices to one-hot vectors, the shape of the results should be (1, ) results = to_categorical(indices, num_classes=78) ### END CODE HERE ### return results, indices

results, indices = predict_and_sample(inference_model, x_initializer, a_initializer, c_initializer) print('np.argmax(results[12]) =', np.argmax(results[12])) print('np.argmax(results[17]) =', np.argmax(results[17])) print('list(indices[12:18]) =', list(indices[12:18])) out_stream = generate_music(inference_model) IPython.display.Audio('./data/30s_trained_model.mp3')

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