Python tensorflow.python.keras.layers.LSTM Examples

The following are 5 code examples of tensorflow.python.keras.layers.LSTM(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.keras.layers , or try the search function .
Example #1
Source File: plot_segment_rep.py    From seglearn with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def crnn_model(width=100, n_vars=6, n_classes=7, conv_kernel_size=5,
               conv_filters=3, lstm_units=3):
    input_shape = (width, n_vars)
    model = Sequential()
    model.add(Conv1D(filters=conv_filters, kernel_size=conv_kernel_size,
                     padding='valid', activation='relu', input_shape=input_shape))
    model.add(Conv1D(filters=conv_filters, kernel_size=conv_kernel_size,
                     padding='valid', activation='relu'))
    model.add(LSTM(units=lstm_units, dropout=0.1, recurrent_dropout=0.1))
    model.add(Dense(n_classes, activation="softmax"))

    model.compile(loss='categorical_crossentropy', optimizer='adam',
                  metrics=['accuracy'])

    return model


# load the data
Example #2
Source File: plot_model_selection2.py    From seglearn with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def crnn_model(width=100, n_vars=6, n_classes=7, conv_kernel_size=5,
               conv_filters=2, lstm_units=2):
    # create a crnn model with keras with one cnn layers, and one rnn layer
    input_shape = (width, n_vars)
    model = Sequential()
    model.add(Conv1D(filters=conv_filters, kernel_size=conv_kernel_size,
                     padding='valid', activation='relu', input_shape=input_shape))
    model.add(LSTM(units=lstm_units, dropout=0.1, recurrent_dropout=0.1))
    model.add(Dense(n_classes, activation="softmax"))

    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

    return model


# load the data
Example #3
Source File: plot_nn_training_curves.py    From seglearn with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def crnn_model(width=100, n_vars=6, n_classes=7, conv_kernel_size=5,
               conv_filters=3, lstm_units=3):
    input_shape = (width, n_vars)
    model = Sequential()
    model.add(Conv1D(filters=conv_filters, kernel_size=conv_kernel_size,
                     padding='valid', activation='relu', input_shape=input_shape))
    model.add(LSTM(units=lstm_units, dropout=0.1, recurrent_dropout=0.1))
    model.add(Dense(n_classes, activation="softmax"))

    model.compile(loss='categorical_crossentropy', optimizer='adam',
                  metrics=['accuracy'])

    return model


##############################################
# Setup
##############################################

# load the data
Example #4
Source File: sequence.py    From DeepCTR with Apache License 2.0 6 votes vote down vote up 
def build(self, input_shape):

        if len(input_shape) != 3:
            raise ValueError(
                "Unexpected inputs dimensions %d, expect to be 3 dimensions" % (len(input_shape)))
        self.fw_lstm = []
        self.bw_lstm = []
        for _ in range(self.layers):
            self.fw_lstm.append(
                LSTM(self.units, dropout=self.dropout_rate, bias_initializer='ones', return_sequences=True,
                     unroll=True))
            self.bw_lstm.append(
                LSTM(self.units, dropout=self.dropout_rate, bias_initializer='ones', return_sequences=True,
                     go_backwards=True, unroll=True))

        super(BiLSTM, self).build(
            input_shape)  # Be sure to call this somewhere!
Example #5
Source File: sequence.py    From icme2019 with MIT License 5 votes vote down vote up 
def build(self, input_shape):

        if len(input_shape) != 3:
            raise ValueError(
                "Unexpected inputs dimensions %d, expect to be 3 dimensions" % (len(input_shape)))
        self.fw_lstm = []
        self.bw_lstm = []
        for _ in range(self.layers):
            self.fw_lstm.append(LSTM(self.units, dropout=self.dropout, bias_initializer='ones', return_sequences=True,
                                     unroll=True))
            self.bw_lstm.append(LSTM(self.units, dropout=self.dropout, bias_initializer='ones', return_sequences=True,
                                     go_backwards=True, unroll=True))

        super(BiLSTM, self).build(
            input_shape)  # Be sure to call this somewhere!