Python keras.layers.pooling.GlobalMaxPooling1D() Examples

The following are 9 code examples of keras.layers.pooling.GlobalMaxPooling1D(). 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 keras.layers.pooling , or try the search function .
Example #1
Source File: convolutional_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_globalpooling_1d():
    layer_test(pooling.GlobalMaxPooling1D,
               input_shape=(3, 4, 5))
    layer_test(pooling.GlobalAveragePooling1D,
               input_shape=(3, 4, 5))
Example #2
Source File: convolutional_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_globalpooling_1d():
    layer_test(pooling.GlobalMaxPooling1D,
               input_shape=(3, 4, 5))
    layer_test(pooling.GlobalAveragePooling1D,
               input_shape=(3, 4, 5))
Example #3
Source File: convolutional_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_globalpooling_1d():
    layer_test(pooling.GlobalMaxPooling1D,
               input_shape=(3, 4, 5))
    layer_test(pooling.GlobalAveragePooling1D,
               input_shape=(3, 4, 5))
Example #4
Source File: convolutional_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_globalpooling_1d():
    layer_test(pooling.GlobalMaxPooling1D,
               input_shape=(3, 4, 5))
    layer_test(pooling.GlobalAveragePooling1D,
               input_shape=(3, 4, 5))
Example #5
Source File: convolutional_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_globalpooling_1d():
    layer_test(pooling.GlobalMaxPooling1D,
               input_shape=(3, 4, 5))
    layer_test(pooling.GlobalAveragePooling1D,
               input_shape=(3, 4, 5))
Example #6
Source File: convolutional_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_globalpooling_1d():
    layer_test(pooling.GlobalMaxPooling1D,
               input_shape=(3, 4, 5))
    layer_test(pooling.GlobalAveragePooling1D,
               input_shape=(3, 4, 5))
Example #7
Source File: convolutional_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_globalpooling_1d():
    layer_test(pooling.GlobalMaxPooling1D,
               input_shape=(3, 4, 5))
    layer_test(pooling.GlobalAveragePooling1D,
               input_shape=(3, 4, 5))
Example #8
Source File: multiclass.py    From intent_classifier with Apache License 2.0 5 votes vote down vote up 
def cnn_model(self, params):
        """
        Method builds uncompiled intent_model of shallow-and-wide CNN
        Args:
            params: disctionary of parameters for NN

        Returns:
            Uncompiled intent_model
        """
        if type(self.opt['kernel_sizes_cnn']) is str:
            self.opt['kernel_sizes_cnn'] = [int(x) for x in
                                            self.opt['kernel_sizes_cnn'].split(' ')]

        inp = Input(shape=(params['text_size'], params['embedding_size']))

        outputs = []
        for i in range(len(params['kernel_sizes_cnn'])):
            output_i = Conv1D(params['filters_cnn'], kernel_size=params['kernel_sizes_cnn'][i],
                              activation=None,
                              kernel_regularizer=l2(params['coef_reg_cnn']),
                              padding='same')(inp)
            output_i = BatchNormalization()(output_i)
            output_i = Activation('relu')(output_i)
            output_i = GlobalMaxPooling1D()(output_i)
            outputs.append(output_i)

        output = concatenate(outputs, axis=1)

        output = Dropout(rate=params['dropout_rate'])(output)
        output = Dense(params['dense_size'], activation=None,
                       kernel_regularizer=l2(params['coef_reg_den']))(output)
        output = BatchNormalization()(output)
        output = Activation('relu')(output)
        output = Dropout(rate=params['dropout_rate'])(output)
        output = Dense(self.n_classes, activation=None,
                       kernel_regularizer=l2(params['coef_reg_den']))(output)
        output = BatchNormalization()(output)
        act_output = Activation('sigmoid')(output)
        model = Model(inputs=inp, outputs=act_output)
        return model
Example #9
Source File: multiclass.py    From intent_classifier with Apache License 2.0 4 votes vote down vote up 
def dcnn_model(self, params):
        """
        Method builds uncompiled intent_model of deep CNN
        Args:
            params: disctionary of parameters for NN

        Returns:
            Uncompiled intent_model
        """
        if type(self.opt['kernel_sizes_cnn']) is str:
            self.opt['kernel_sizes_cnn'] = [int(x) for x in
                                            self.opt['kernel_sizes_cnn'].split(' ')]

        if type(self.opt['filters_cnn']) is str:
            self.opt['filters_cnn'] = [int(x) for x in
                                       self.opt['filters_cnn'].split(' ')]

        inp = Input(shape=(params['text_size'], params['embedding_size']))

        output = inp

        for i in range(len(params['kernel_sizes_cnn'])):
            output = Conv1D(params['filters_cnn'][i], kernel_size=params['kernel_sizes_cnn'][i],
                            activation=None,
                            kernel_regularizer=l2(params['coef_reg_cnn']),
                            padding='same')(output)
            output = BatchNormalization()(output)
            output = Activation('relu')(output)
            output = MaxPooling1D()(output)

        output = GlobalMaxPooling1D()(output)
        output = Dropout(rate=params['dropout_rate'])(output)
        output = Dense(params['dense_size'], activation=None,
                       kernel_regularizer=l2(params['coef_reg_den']))(output)
        output = BatchNormalization()(output)
        output = Activation('relu')(output)
        output = Dropout(rate=params['dropout_rate'])(output)
        output = Dense(self.n_classes, activation=None,
                       kernel_regularizer=l2(params['coef_reg_den']))(output)
        output = BatchNormalization()(output)
        act_output = Activation('sigmoid')(output)
        model = Model(inputs=inp, outputs=act_output)
        return model