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title: ‘DeepLearning.ai作业:(5-1)-- 循环神经网络(Recurrent Neural Networks)(3)’
id: dl-ai-5-1h3 tags:第三个作业是用LSTM来生成爵士乐。
我们已经对音乐数据做了预处理,以”values”来表示。可以非正式地将每个”value”看作一个音符,它包含音高和持续时间。 例如,如果您按下特定钢琴键0.5秒,那么您刚刚弹奏了一个音符。 在音乐理论中,”value” 实际上比这更复杂。 特别是,它还捕获了同时播放多个音符所需的信息。 例如,在播放音乐作品时,可以同时按下两个钢琴键(同时播放多个音符生成所谓的“和弦”)。 但是这里我们不需要关系音乐理论的细节。对于这个作业,你需要知道的是,我们获得一个”values”的数据集,并将学习一个RNN模型来生成一个序列的”values”。
我们的音乐生成系统将使用78个独特的值。
模型结构如下:
这里用了3个keras函数来定义:
reshapor = Reshape((1, 78)) # Used in Step 2.B of djmodel(), belowLSTM_cell = LSTM(n_a, return_state = True) # Used in Step 2.Cdensor = Dense(n_values, activation='softmax') # Used in Step 2.D
# GRADED FUNCTION: djmodeldef 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 = 60a0 = np.zeros((m, n_a))c0 = np.zeros((m, n_a))
model.fit([X, a0, c0], list(Y), epochs=100)
# GRADED FUNCTION: music_inference_modeldef 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_sampledef 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=x_initializer.shape[-1]) ### END CODE HERE ### return results, indices
out_stream = generate_music(inference_model)
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