自学教程：Python layers.Masking方法代码示例

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Masking [as 别名]def get_audio_model(self):		# Modality specific hyperparameters		self.epochs = 100		self.batch_size = 50		# Modality specific parameters		self.embedding_dim = self.train_x.shape[2]		print("Creating Model...")				inputs = Input(shape=(self.sequence_length, self.embedding_dim), dtype='float32')		masked = Masking(mask_value =0)(inputs)		lstm = Bidirectional(LSTM(300, activation='tanh', return_sequences = True, dropout=0.4))(masked)		lstm = Bidirectional(LSTM(300, activation='tanh', return_sequences = True, dropout=0.4), name="utter")(lstm)		output = TimeDistributed(Dense(self.classes,activation='softmax'))(lstm)		model = Model(inputs, output)		return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Masking [as 别名]def _build_model(self, num_features, num_actions, max_history_len):        """Build a keras model and return a compiled model.        :param max_history_len: The maximum number of historical turns used to                                decide on next action"""        from keras.layers import LSTM, Activation, Masking, Dense        from keras.models import Sequential        n_hidden = 32  # size of hidden layer in LSTM        # Build Model        batch_shape = (None, max_history_len, num_features)        model = Sequential()        model.add(Masking(-1, batch_input_shape=batch_shape))        model.add(LSTM(n_hidden, batch_input_shape=batch_shape))        model.add(Dense(input_dim=n_hidden, output_dim=num_actions))        model.add(Activation('softmax'))        model.compile(loss='categorical_crossentropy',                      optimizer='adam',                      metrics=['accuracy'])        logger.debug(model.summary())        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Masking [as 别名]def _build_model(self, num_features, num_actions, max_history_len):        """Build a keras model and return a compiled model.        :param max_history_len: The maximum number of historical                                turns used to decide on next action        """        from keras.layers import LSTM, Activation, Masking, Dense        from keras.models import Sequential        n_hidden = 32  # Neural Net and training params        batch_shape = (None, max_history_len, num_features)        # Build Model        model = Sequential()        model.add(Masking(-1, batch_input_shape=batch_shape))        model.add(LSTM(n_hidden, batch_input_shape=batch_shape))        model.add(Dense(input_dim=n_hidden, units=num_actions))        model.add(Activation('softmax'))        model.compile(loss='categorical_crossentropy',                      optimizer='rmsprop',                      metrics=['accuracy'])        logger.debug(model.summary())        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Masking [as 别名]def model_architecture(self, num_features, num_actions, max_history_len):        """Build a Keras model and return a compiled model."""        from keras.layers import LSTM, Activation, Masking, Dense        from keras.models import Sequential        n_hidden = 32  # size of hidden layer in LSTM        # Build Model        batch_shape = (None, max_history_len, num_features)        model = Sequential()        model.add(Masking(-1, batch_input_shape=batch_shape))        model.add(LSTM(n_hidden, batch_input_shape=batch_shape))        model.add(Dense(input_dim=n_hidden, output_dim=num_actions))        model.add(Activation("softmax"))        model.compile(loss="categorical_crossentropy",                      optimizer="adam",                      metrics=["accuracy"])        logger.debug(model.summary())        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Masking [as 别名]def create_network(nb_features, nb_labels, padding_value):    # Define the network architecture    input_data = Input(name='input', shape=(None, nb_features)) # nb_features = image height    masking = Masking(mask_value=padding_value)(input_data)    noise = GaussianNoise(0.01)(masking)    blstm = Bidirectional(LSTM(128, return_sequences=True, dropout=0.1))(noise)    blstm = Bidirectional(LSTM(128, return_sequences=True, dropout=0.1))(blstm)    blstm = Bidirectional(LSTM(128, return_sequences=True, dropout=0.1))(blstm)    dense = TimeDistributed(Dense(nb_labels + 1, name="dense"))(blstm)    outrnn = Activation('softmax', name='softmax')(dense)    network = CTCModel([input_data], [outrnn])    network.compile(Adam(lr=0.0001))    return network 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Masking [as 别名]def creat_model(input_shape, num_class):    init = initializers.Orthogonal(gain=args.norm)    sequence_input =Input(shape=input_shape)    mask = Masking(mask_value=0.)(sequence_input)    if args.aug:        mask = augmentaion()(mask)    X = Noise(0.075)(mask)    if args.model[0:2]=='VA':        # VA        trans = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X)        trans = Dropout(0.5)(trans)        trans = TimeDistributed(Dense(3,kernel_initializer='zeros'))(trans)        rot = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X)        rot = Dropout(0.5)(rot)        rot = TimeDistributed(Dense(3,kernel_initializer='zeros'))(rot)        transform = Concatenate()([rot,trans])        X = VA()([mask,transform])    X = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X)    X = Dropout(0.5)(X)    X = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X)    X = Dropout(0.5)(X)    X = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X)    X = Dropout(0.5)(X)    X = TimeDistributed(Dense(num_class))(X)    X = MeanOverTime()(X)    X = Activation('softmax')(X)    model=Model(sequence_input,X)    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Masking [as 别名]def create_model(self):        model = Sequential()        #model.add(Masking(mask_value=0, input_shape=(1, self.settings.getint("LSTM", "max_vector_length"))))        model.add(LSTM_CELL(self.settings.getint("LSTM", "hidden_layers"),                            input_shape=(self.settings.getint("LSTM", "time_series"), self.settings.getint("LSTM", "max_vector_length")),                            return_sequences=True))        model.add(LSTM_CELL(self.settings.getint("LSTM", "hidden_layers")))        model.add(Dropout(self.settings.getfloat("LSTM", "dropout")))        model.add(Dense(self.settings.getint('LSTM', 'max_vector_length')))        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Masking [as 别名]def learn_model(self, features, labels, degrade_mask, epochs=30, batch_size=None, model=None):        print('learning model')        if True or not model and not self.model:            model = Sequential()            masking = Masking(mask_value=0.0, input_shape=(features.shape[1], features.shape[2],))            model.add(masking)            crf = CRF(#input_shape=(features.shape[1], features.shape[2],),                      units=labels.shape[-1],                      sparse_target=False,                      kernel_regularizer=keras.regularizers.l1_l2(0.0001, 0.0001),                      #bias_regularizer=keras.regularizers.l2(0.005),                      #chain_regularizer=keras.regularizers.l2(0.005),                      #boundary_regularizer=keras.regularizers.l2(0.005),                      learn_mode='marginal',                      test_mode='marginal',                      unroll=self.unroll_flag,                     )            model.add(crf)            model.compile(optimizer=self.opt,                          loss=crf_loss,                          #loss=crf.loss_function,                          metrics=[crf_accuracy],                          #metrics=[crf.accuracy],                          )        elif self.model:            model = self.model        else:            assert model        #assert features.shape[0] == len(self.degrade_mask)        #weights = self._weight_logic(features, degrade_mask)        model.fit(features,                  labels,                  epochs=epochs,                  batch_size=batch_size,                  verbose=1,                  #sample_weight=weights,                  )        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Masking [as 别名]def assemble_rnn(params, final_reshape=True):    """Construct an RNN/LSTM/GRU model of the form: X-[H1-H2-...-HN]-Y.    All the H-layers are optional recurrent layers and depend on whether they    are specified in the params dictionary.    """    # Input layer    input_shape = params['input_shape']    inputs = layers.Input(shape=input_shape)    # inputs = layers.Input(batch_shape=[20] + list(input_shape))    # Masking layer    previous = layers.Masking(mask_value=0.0)(inputs)    # Hidden layers    for layer in params['hidden_layers']:        Layer = layers.deserialize(            {'class_name': layer['name'], 'config': layer['config']})        previous = Layer(previous)        if 'dropout' in layer and layer['dropout'] is not None:            previous = layers.Dropout(layer['dropout'])(previous)        if 'batch_norm' in layer and layer['batch_norm'] is not None:            previous = layers.BatchNormalization(**layer['batch_norm'])(previous)    # Output layer    output_shape = params['output_shape']    output_dim = np.prod(output_shape)    outputs = layers.Dense(output_dim)(previous)    if final_reshape:        outputs = layers.Reshape(output_shape)(outputs)    return KerasModel(inputs=inputs, outputs=outputs) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Masking [as 别名]def test_merge_mask_2d():    rand = lambda *shape: np.asarray(np.random.random(shape) > 0.5, dtype='int32')    # inputs    input_a = layers.Input(shape=(3,))    input_b = layers.Input(shape=(3,))    # masks    masked_a = layers.Masking(mask_value=0)(input_a)    masked_b = layers.Masking(mask_value=0)(input_b)    # three different types of merging    merged_sum = legacy_layers.merge([masked_a, masked_b], mode='sum')    merged_concat = legacy_layers.merge([masked_a, masked_b], mode='concat', concat_axis=1)    merged_concat_mixed = legacy_layers.merge([masked_a, input_b], mode='concat', concat_axis=1)    # test sum    model_sum = models.Model([input_a, input_b], [merged_sum])    model_sum.compile(loss='mse', optimizer='sgd')    model_sum.fit([rand(2, 3), rand(2, 3)], [rand(2, 3)], epochs=1)    # test concatenation    model_concat = models.Model([input_a, input_b], [merged_concat])    model_concat.compile(loss='mse', optimizer='sgd')    model_concat.fit([rand(2, 3), rand(2, 3)], [rand(2, 6)], epochs=1)    # test concatenation with masked and non-masked inputs    model_concat = models.Model([input_a, input_b], [merged_concat_mixed])    model_concat.compile(loss='mse', optimizer='sgd')    model_concat.fit([rand(2, 3), rand(2, 3)], [rand(2, 6)], epochs=1) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Masking [as 别名]def test_masking():    layer_test(layers.Masking,               kwargs={},               input_shape=(3, 2, 3)) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Masking [as 别名]def test_masking():    np.random.seed(1337)    x = np.array([[[1], [1]],                  [[0], [0]]])    model = Sequential()    model.add(Masking(mask_value=0, input_shape=(2, 1)))    model.add(TimeDistributed(Dense(1, kernel_initializer='one')))    model.compile(loss='mse', optimizer='sgd')    y = np.array([[[1], [1]],                  [[1], [1]]])    loss = model.train_on_batch(x, y)    assert loss == 0