title: ‘DeepLearning.ai作業:(5-3) – 序列模型和注意力機制’ id: dl-ai-5-3h tags:

• dl.ai
• homework categories:
• AI
• Deep Learning date: 2018-10-18 18:39:15

這周作業分為了兩部分：

• 機器翻譯
• 觸發關鍵字

Part1：機器翻譯

你將建立一個將人類可讀日期（“2009年6月25日”）轉換為機器可讀日期（“2009-06-25”）的神經機器翻譯（NMT）模型。 你將使用注意力機制來執行此操作，這是模型序列中最尖端的一個序列。

你將建立的模型可用於從一種語言翻譯為另一種語言，如從英語翻譯為印地安語。 但是，語言翻譯需要大量的資料集，並且通常需要幾天的GPU訓練。 在不使用海量資料的情況下，為了讓你有機會嘗試使用這些模型，我們使用更簡單的“日期轉換”任務。

網路以各種可能格式（例如“1958年8月29日”，“03/30/1968”，“1987年6月24日”）寫成的日期作為輸入，並將它們轉換成標準化的機器可讀的日期（例如“1958 -08-29“，”1968-03-30“，”1987-06-24“），讓網路學習以通用機器可讀格式YYYY-MM-DD輸出日期。

• X: 經過處理的訓練集中人類可讀日期，其中每個字元都替換為其在human_vocab中對映到的索引。 每個日期用特殊字元進一步填充為Tx長度。 X.shape =（m，Tx）
• Y: 經過處理的訓練集中機器可讀日期，其中每個字元都替換為其在machine_vocab中對映到的索引。 你應該有Y.shape =（m，Ty）。
• Xoh：X的one-hot向量，Xoh.shape = (m，Tx，len(human_vocab))
• Yoh：Y的one-hot向量，Yoh.shape = (m，Tx，len(machine_vocab))

採用注意力機制的機器翻譯

定義一些layers

# Defined shared layers as global variables
repeator = RepeatVector(Tx)
concatenator = Concatenate(axis=-1)
densor1 = Dense(10, activation = "tanh")
densor2 = Dense(1, activation =
 
 "relu")
activator = Activation(softmax, name='attention_weights') # We are using a custom softmax(axis = 1) loaded in this notebook
dotor = Dot(axes = 1)

然後根據a 和 s 得到context

# GRADED FUNCTION: one_step_attention

def one_step_attention(a, s_prev):
    """
    Performs one step of attention: Outputs a context vector computed as a dot product of the attention weights
    "alphas" and the hidden states "a" of the Bi-LSTM.
    
    Arguments:
    a -- hidden state output of the Bi-LSTM, numpy-array of shape (m, Tx, 2*n_a)
    s_prev -- previous hidden state of the (post-attention) LSTM, numpy-array of shape (m, n_s)
    
    Returns:
    context -- context vector, input of the next (post-attetion) LSTM cell
    """
    
    ### START CODE HERE ###
    # Use repeator to repeat s_prev to be of shape (m, Tx, n_s) so that you can concatenate it with all hidden states "a" (≈ 1 line)
    s_prev = repeator(s_prev)
    # Use concatenator to concatenate a and s_prev on the last axis (≈ 1 line)
    concat = concatenator([a, s_prev])
    # Use densor1 to propagate concat through a small fully-connected neural network to compute the "intermediate energies" variable e. (≈1 lines)
    e = densor1(concat)
    # Use densor2 to propagate e through a small fully-connected neural network to compute the "energies" variable energies. (≈1 lines)
    energies = densor2(e)
    # Use "activator" on "energies" to compute the attention weights "alphas" (≈ 1 line)
    alphas = activator(energies)
    # Use dotor together with "alphas" and "a" to compute the context vector to be given to the next (post-attention) LSTM-cell (≈ 1 line)
    context = dotor([alphas, a])
    ### END CODE HERE ###
    
    return context

實現model()

n_a = 32
n_s = 64
post_activation_LSTM_cell = LSTM(n_s, return_state = True)
output_layer = Dense(len(machine_vocab), activation=softmax)

# GRADED FUNCTION: model

def model(Tx, Ty, n_a, n_s, human_vocab_size, machine_vocab_size):
    """
    Arguments:
    Tx -- length of the input sequence
    Ty -- length of the output sequence
    n_a -- hidden state size of the Bi-LSTM
    n_s -- hidden state size of the post-attention LSTM
    human_vocab_size -- size of the python dictionary "human_vocab"
    machine_vocab_size -- size of the python dictionary "machine_vocab"

    Returns:
    model -- Keras model instance
    """
    
    # Define the inputs of your model with a shape (Tx,)
    # Define s0 and c0, initial hidden state for the decoder LSTM of shape (n_s,)
    X = Input(shape=(Tx, human_vocab_size))
    s0 = Input(shape=(n_s,), name='s0')
    c0 = Input(shape=(n_s,), name='c0')
    s = s0
    c = c0
    
    # Initialize empty list of outputs
    outputs = []
    
    ### START CODE HERE ###
    
    # Step 1: Define your pre-attention Bi-LSTM. Remember to use return_sequences=True. (≈ 1 line)
    a = Bidirectional(LSTM(n_a, return_sequences=True))(X)
    
    # Step 2: Iterate for Ty steps
    for t in range(Ty):
    
        # Step 2.A: Perform one step of the attention mechanism to get back the context vector at step t (≈ 1 line)
        context = one_step_attention(a ,s)
        
        # Step 2.B: Apply the post-attention LSTM cell to the "context" vector.
        # Don't forget to pass: initial_state = [hidden state, cell state] (≈ 1 line)
        s, _, c = post_activation_LSTM_cell(context, initial_state=[s, c])
        
        # Step 2.C: Apply Dense layer to the hidden state output of the post-attention LSTM (≈ 1 line)
        out = output_layer(s)
        
        # Step 2.D: Append "out" to the "outputs" list (≈ 1 line)
        outputs.append(out)
    
    # Step 3: Create model instance taking three inputs and returning the list of outputs. (≈ 1 line)
    model = Model(inputs=[X,s0,c0], outputs=outputs)
    
    ### END CODE HERE ###
    
    return model

Part2:Trigger Word Detection

做觸發關鍵字的檢測。

X: 這裡把每一段音訊分為了10s，而10s內細分為了5511個小的片段，也就是Tx = 5511

Y: Ty = 1375，每個y都是一個布林值，用來記錄有沒有收到觸發關鍵字。

生成一個訓練示例

這裡把樣本分為了三種，背景音樂，正向的音訊，反向的音訊，合成訓練示例：

• 隨機選擇一個10秒的背景音訊剪輯
• 隨機將0-4個正向音訊片段插入此10秒剪輯中
• 隨機將0-2個反向音訊片段插入此10秒剪輯中

合成後類似這樣：

定義一個隨機插入片段起始和終點位置的函式：

def get_random_time_segment(segment_ms):
    """
    Gets a random time segment of duration segment_ms in a 10,000 ms audio clip.
    
    Arguments:
    segment_ms -- the duration of the audio clip in ms ("ms" stands for "milliseconds")
    
    Returns:
    segment_time -- a tuple of (segment_start, segment_end) in ms
    """
    
    segment_start = np.random.randint(low=0, high=10000-segment_ms)   # Make sure segment doesn't run past the 10sec background 
    segment_end = segment_start + segment_ms - 1
    
    return (segment_start, segment_end)

然後需要判斷在別的片段插入的時候，有沒有被佔用:

# GRADED FUNCTION: is_overlapping

def is_overlapping(segment_time, previous_segments):
    """
    Checks if the time of a segment overlaps with the times of existing segments.
    
    Arguments:
    segment_time -- a tuple of (segment_start, segment_end) for the new segment
    previous_segments -- a list of tuples of (segment_start, segment_end) for the existing segments
    
    Returns:
    True if the time segment overlaps with any of the existing segments, False otherwise
    """
    
    segment_start, segment_end = segment_time
    
    ### START CODE HERE ### (≈ 4 line)
    # Step 1: Initialize overlap as a "False" flag. (≈ 1 line)
    overlap = False
    
    # Step 2: loop over the previous_segments start and end times.
    # Compare start/end times and set the flag to True if there is an overlap (≈ 3 lines)
    for previous_start, previous_end in previous_segments:
        if segment_start <= previous_end and segment_end >= previous_start:
            overlap = True
    ### END CODE HERE ###

    return overlap

生成input音訊片段：

# GRADED FUNCTION: insert_audio_clip

def insert_audio_clip(background, audio_clip, previous_segments):
    """
    Insert a new audio segment over the background noise at a random time step, ensuring that the 
    audio segment does not overlap with existing segments.
    
    Arguments:
    background -- a 10 second background audio recording.  
    audio_clip -- the audio clip to be inserted/overlaid. 
    previous_segments -- times where audio segments have already been placed
    
    Returns:
    new_background -- the updated background audio
    """
    
    # Get the duration of the audio clip in ms
    segment_ms = len(audio_clip)
    
    ### START CODE HERE ### 
    # Step 1: Use one of the helper functions to pick a random time segment onto which to insert 
    # the new audio clip. (≈ 1 line)
    segment_time = get_random_time_segment(segment_ms)
    
    # Step 2: Check if the new segment_time overlaps with one of the previous_segments. If so, keep 
    # picking new segment_time at random until it doesn't overlap. (≈ 2 lines)
    while is_overlapping(segment_time,previous_segments):
        segment_time = get_random_time_segment(segment_ms)

    # Step 3: Add the new segment_time to the list of previous_segments (≈ 1 line)
    previous_segments.append(segment_time)
    ### END CODE HERE ###
    
    # Step 4: Superpose audio segment and background
    new_background = background.overlay(audio_clip, position = segment_time[0])
    
    return new_background, segment_time

生成y標籤：

# GRADED FUNCTION: insert_ones

def insert_ones(y, segment_end_ms):
    """
    Update the label vector y. The labels of the 50 output steps strictly after the end of the segment 
    should be set to 1. By strictly we mean that the label of segment_end_y should be 0 while, the
    50 followinf labels should be ones.
    
    
    Arguments:
    y -- numpy array of shape (1, Ty), the labels of the training example
    segment_end_ms -- the end time of the segment in ms
    
    Returns:
    y -- updated labels
    """
    
    # duration of the background (in terms of spectrogram time-steps)
    segment_end_y = int(segment_end_ms * Ty / 10000.0)
    
    # Add 1 to the correct index in the background label (y)
    ### START CODE HERE ### (≈ 3 lines)
    for i in range(segment_end_y+1, segment_end_y+51):
        if i < Ty:
            y[0, i] = 1
    ### END CODE HERE ###
    
    return y

# GRADED FUNCTION: create_training_example

def create_training_example(background, activates, negatives):
    """
    Creates a training example with a given background, activates, and negatives.
    
    Arguments:
    background -- a 10 second background audio recording
    activates -- a list of audio segments of the word "activate"
    negatives -- a list of audio segments of random words that are not "activate"
    
    Returns:
    x -- the spectrogram of the training example
    y -- the label at each time step of the spectrogram
    """
    
    # Set the random seed
    np.random.seed(18)
    
    # Make background quieter
    background = background - 20

    ### START CODE HERE ###
    # Step 1: Initialize y (label vector) of zeros (≈ 1 line)
    y = np.zeros((1, Ty))

    # Step 2: Initialize segment times as empty list (≈ 1 line)
    previous_segments = []
    ### END CODE HERE ###
    
    # Select 0-4 random "activate" audio clips from the entire list of "activates" recordings
    number_of_activates = np.random.randint(0, 5)
    random_indices = np.random.randint(len(activates), size=number_of_activates)
    random_activates = [activates[i] for i in random_indices]
    
    ### START CODE HERE ### (≈ 3 lines)
    # Step 3: Loop over randomly selected "activate" clips and insert in background
    for random_activate in random_activates:
        # Insert the audio clip on the background
        background, segment_time = insert_audio_clip(background, random_activate, previous_segments)
        # Retrieve segment_start and segment_end from segment_time
        segment_start, segment_end = segment_time
        # Insert labels in "y"
        y = insert_ones(y, segment_end)
    ### END CODE HERE ###

    # Select 0-2 random negatives audio recordings from the entire list of "negatives" recordings
    number_of_negatives = np.random.randint(0, 3)
    random_indices = np.random.randint(len(negatives), size=number_of_negatives)
    random_negatives = [negatives[i] for i in random_indices]

    ### START CODE HERE ### (≈ 2 lines)
    # Step 4: Loop over randomly selected negative clips and insert in background
    for random_negative in random_negatives:
        # Insert the audio clip on the background 
        background, _ = insert_audio_clip(background, random_negative, previous_segments)
    ### END CODE HERE ###
    
    # Standardize the volume of the audio clip 
    background = match_target_amplitude(background, -20.0)

    # Export new training example 
    file_handle = background.export("train" + ".wav", format="wav")
    print("File (train.wav) was saved in your directory.")
    
    # Get and plot spectrogram of the new recording (background with superposition of positive and negatives)
    x = graph_spectrogram("train.wav")
    
    return x, y

實現model()

# GRADED FUNCTION: model

def model(input_shape):
    """
    Function creating the model's graph in Keras.
    
    Argument:
    input_shape -- shape of the model's input data (using Keras conventions)

    Returns:
    model -- Keras model instance
    """
    
    X_input = Input(shape = input_shape)
    
    ### START CODE HERE ###
    
    # Step 1: CONV layer (≈4 lines)
    X = Conv1D(filters=196,kernel_size=15,strides=4)(X_input)                                 # CONV1D
    X = BatchNormalization()(X)                                 # Batch normalization
    X = Activation('relu')(X)                                 # ReLu activation
    X = Dropout(0.8)(X)                                 # dropout (use 0.8)

    # Step 2: First GRU Layer (≈4 lines)
    X = GRU(units = 128, return_sequences = True)(X)                                 # GRU (use 128 units and return the sequences)
    X = Dropout(0.8)(X)                                  # dropout (use 0.8)
    X = BatchNormalization()(X)                                 # Batch normalization
    
    # Step 3: Second GRU Layer (≈4 lines)
    X = GRU(units = 128, return_sequences = True)(X)                                 # GRU (use 128 units and return the sequences)
    X = Dropout(0.8)(X)                                  # dropout (use 0.8)
    X = BatchNormalization()(X)                                 # Batch normalization
    X = Dropout(0.8)(X)                                 # dropout (use 0.8)
    
    # Step 4: Time-distributed dense layer (≈1 line)
    X = TimeDistributed(Dense(1, activation = "sigmoid"))(X) # time distributed  (sigmoid)

    ### END CODE HERE ###

    model = Model(inputs = X_input, outputs = X)
    
    return model  

這裡載入預訓練好的模型，不需要自己訓練那麼久了，

model = load_model('./models/tr_model.h5')
opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=["accuracy"])
model.fit(X, Y, batch_size = 5, epochs=1)