Python keras.layers.core.Activation() Examples

def test_temporal_clf(self):
        print('temporal classification data:')
        (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(5,10), 
                                                             classification=True, nb_class=2)
        print('X_train:', X_train.shape)
        print('X_test:', X_test.shape)
        print('y_train:', y_train.shape)
        print('y_test:', y_test.shape)

        y_train = to_categorical(y_train)
        y_test = to_categorical(y_test)

        model = Sequential()
        model.add(GRU(X_train.shape[-1], y_train.shape[-1]))
        model.add(Activation('softmax'))
        model.compile(loss='categorical_crossentropy', optimizer='adadelta')
        history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2)
        self.assertTrue(history.history['val_acc'][-1] > 0.9) 

def build_model():
    """
    定义模型
    """
    model = Sequential()

    model.add(LSTM(units=Conf.LAYERS[1], input_shape=(Conf.LAYERS[1], Conf.LAYERS[0]), return_sequences=True))
    model.add(Dropout(0.2))

    model.add(LSTM(Conf.LAYERS[2], return_sequences=False))
    model.add(Dropout(0.2))

    model.add(Dense(units=Conf.LAYERS[3]))
    # model.add(BatchNormalization(weights=None, epsilon=1e-06, momentum=0.9))
    model.add(Activation("tanh"))
    # act = PReLU(alpha_initializer='zeros', weights=None)
    # act = LeakyReLU(alpha=0.3)
    # model.add(act)

    start = time.time()
    model.compile(loss="mse", optimizer="rmsprop")
    print("> Compilation Time : ", time.time() - start)
    return model 

def __transition_block(ip, nb_filter, compression=1.0, weight_decay=1e-4):
    ''' Apply BatchNorm, Relu 1x1, Conv2D, optional compression, dropout and Maxpooling2D
    Args:
        ip: keras tensor
        nb_filter: number of filters
        compression: calculated as 1 - reduction. Reduces the number of feature maps
                    in the transition block.
        dropout_rate: dropout rate
        weight_decay: weight decay factor
    Returns: keras tensor, after applying batch_norm, relu-conv, dropout, maxpool
    '''
    concat_axis = 1 if K.image_data_format() == 'channels_first' else -1

    x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(ip)
    x = Activation('relu')(x)
    x = Conv2D(int(nb_filter * compression), (1, 1), kernel_initializer='he_normal', padding='same', use_bias=False,
               kernel_regularizer=l2(weight_decay))(x)
    x = AveragePooling2D((2, 2), strides=(2, 2))(x)

    # global context block
    x = global_context_block(x)

    return x 

def identity_block(input_tensor, filters, d_rates):
    x = Conv2D(filters[0], kernel_size=1, dilation_rate=d_rates[0])(input_tensor)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters[1], kernel_size=3, padding='same', dilation_rate=d_rates[1])(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters[2], kernel_size=1, dilation_rate=d_rates[2])(x)
    x = BatchNormalization()(x)

    x = add([x, input_tensor])
    x = Activation('relu')(x)

    return x 

def conv_block(input_tensor, filters, strides, d_rates):
    x = Conv2D(filters[0], kernel_size=1, dilation_rate=d_rates[0])(input_tensor)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters[1], kernel_size=3, strides=strides, padding='same', dilation_rate=d_rates[1])(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters[2], kernel_size=1, dilation_rate=d_rates[2])(x)
    x = BatchNormalization()(x)

    shortcut = Conv2D(filters[2], kernel_size=1, strides=strides)(input_tensor)
    shortcut = BatchNormalization()(shortcut)

    x = add([x, shortcut])
    x = Activation('relu')(x)

    return x 

def identity_block(input_tensor, filters, d_rates):
    x = Conv2D(filters[0], kernel_size=1, dilation_rate=d_rates[0])(input_tensor)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters[1], kernel_size=3, padding='same', dilation_rate=d_rates[1])(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters[2], kernel_size=1, dilation_rate=d_rates[2])(x)
    x = BatchNormalization()(x)

    x = add([x, input_tensor])
    x = Activation('relu')(x)

    return x 

def conv_block(input_tensor, filters, strides, d_rates):
    x = Conv2D(filters[0], kernel_size=1, dilation_rate=d_rates[0])(input_tensor)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters[1], kernel_size=3, strides=strides, padding='same', dilation_rate=d_rates[1])(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters[2], kernel_size=1, dilation_rate=d_rates[2])(x)
    x = BatchNormalization()(x)

    shortcut = Conv2D(filters[2], kernel_size=1, strides=strides)(input_tensor)
    shortcut = BatchNormalization()(shortcut)

    x = add([x, shortcut])
    x = Activation('relu')(x)

    return x 

def model_create(input_shape, num_classes):
        logging.debug('input_shape {}'.format(input_shape))

        model = Sequential()

        model.add(Conv2D(32, (3, 3), border_mode='same', input_shape=input_shape))
        model.add(Activation('relu'))

        model.add(Conv2D(32, (3, 3)))
        model.add(Activation('relu'))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.5))

        model.add(Flatten())
        model.add(Dense(128))
        model.add(Activation('relu'))
        model.add(Dropout(0.5))

        model.add(Dense(num_classes))
        model.add(Activation('softmax'))

        # use binary_crossentropy if has just 2 prediction yes or no
        model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])

        return model 

def get_residual_model(is_mnist=True, img_channels=1, img_rows=28, img_cols=28):
    model = keras.models.Sequential()
    first_layer_channel = 128
    if is_mnist: # size to be changed to 32,32
        model.add(ZeroPadding2D((2,2), input_shape=(img_channels, img_rows, img_cols))) # resize (28,28)-->(32,32)
        # the first conv 
        model.add(Convolution2D(first_layer_channel, 3, 3, border_mode='same'))
    else:
        model.add(Convolution2D(first_layer_channel, 3, 3, border_mode='same', input_shape=(img_channels, img_rows, img_cols)))

    model.add(Activation('relu'))
    # [residual-based Conv layers]
    residual_blocks = design_for_residual_blocks(num_channel_input=first_layer_channel)
    model.add(residual_blocks)
    model.add(BatchNormalization(axis=1))
    model.add(Activation('relu'))
    # [Classifier]    
    model.add(Flatten())
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    # [END]
    return model 

def identity_block(input_tensor, filters, d_rates):
    x = Conv2D(filters[0], kernel_size=1, dilation_rate=d_rates[0])(input_tensor)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters[1], kernel_size=3, padding='same', dilation_rate=d_rates[1])(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters[2], kernel_size=1, dilation_rate=d_rates[2])(x)
    x = BatchNormalization()(x)

    x = add([x, input_tensor])
    x = Activation('relu')(x)

    return x 

def conv_block(input_tensor, filters, strides, d_rates):
    x = Conv2D(filters[0], kernel_size=1, dilation_rate=d_rates[0])(input_tensor)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters[1], kernel_size=3, strides=strides, padding='same', dilation_rate=d_rates[1])(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters[2], kernel_size=1, dilation_rate=d_rates[2])(x)
    x = BatchNormalization()(x)

    shortcut = Conv2D(filters[2], kernel_size=1, strides=strides)(input_tensor)
    shortcut = BatchNormalization()(shortcut)

    x = add([x, shortcut])
    x = Activation('relu')(x)

    return x 

def create(self):
        self._input_name = 'text'
        self._output_name = 'output'

        self.add_input(
                name=self._input_name, 
                input_shape=(self._config.max_input_time_steps, self._config.input_dim,))
        self.inputs['text'].input = T.imatrix()
        self.add_node(Embedding(
                self._config.input_dim, 
                self._config.textual_embedding_dim, 
                mask_zero=True), 
                name='embedding', input='text')
        self.add_node(
                self._config.recurrent_encoder(
                    self._config.hidden_state_dim, 
                    return_sequences=False,
                    go_backwards=self._config.go_backwards),
                name='recurrent', input='embedding') 
        self.add_node(Dropout(0.5), name='dropout', input='recurrent')
        self.add_node(Dense(self._config.output_dim), name='dense', input='dropout')
        self.add_node(Activation('softmax'), name='softmax', input='dense')
        self.add_output(name=self._output_name, input='softmax') 

def test_1o_1i_2(self):
        print('test a more complex non-sequential graph with 1 input and 1 output')
        graph = Graph()
        graph.add_input(name='input1', ndim=2)

        graph.add_node(Dense(32, 16), name='dense1', input='input1')
        graph.add_node(Dense(32, 4), name='dense2-0', input='input1')
        graph.add_node(Activation('relu'), name='dense2', input='dense2-0')

        graph.add_node(Dense(4, 16), name='dense3', input='dense2')
        graph.add_node(Dense(16, 4), name='dense4', inputs=['dense1', 'dense3'], merge_mode='sum')

        graph.add_output(name='output1', inputs=['dense2', 'dense4'], merge_mode='sum')
        graph.compile('rmsprop', {'output1': 'mse'})

        history = graph.fit({'input1': X_train, 'output1': y_train}, nb_epoch=10)
        out = graph.predict({'input1': X_train})
        assert(type(out == dict))
        assert(len(out) == 1)
        loss = graph.test_on_batch({'input1': X_test, 'output1': y_test})
        loss = graph.train_on_batch({'input1': X_test, 'output1': y_test})
        loss = graph.evaluate({'input1': X_test, 'output1': y_test})
        print(loss)
        assert(loss < 2.5)
        graph.get_config(verbose=1) 

def build_model(layers):
    """
    模型定义
    """
    model = Sequential()

    model.add(LSTM(units=layers[1], input_shape=(layers[1], layers[0]), return_sequences=True))
    model.add(Dropout(0.2))

    model.add(LSTM(layers[2], return_sequences=False))
    model.add(Dropout(0.2))

    model.add(Dense(units=layers[3]))
    model.add(Activation("tanh"))

    start = time.time()
    model.compile(loss="mse", optimizer="rmsprop")
    print("> Compilation Time : ", time.time() - start)
    return model 

def build_model():
    """
    定义模型
    """
    model = Sequential()

    model.add(LSTM(units=Conf.LAYERS[1], input_shape=(Conf.LAYERS[1], Conf.LAYERS[0]), return_sequences=True))
    model.add(Dropout(0.2))

    model.add(LSTM(Conf.LAYERS[2], return_sequences=False))
    model.add(Dropout(0.2))

    model.add(Dense(units=Conf.LAYERS[3]))
    # model.add(BatchNormalization(weights=None, epsilon=1e-06, momentum=0.9))
    model.add(Activation("tanh"))
    # act = PReLU(alpha_initializer='zeros', weights=None)
    # act = LeakyReLU(alpha=0.3)
    # model.add(act)

    start = time.time()
    model.compile(loss="mse", optimizer="rmsprop")
    print("> Compilation Time : ", time.time() - start)
    return model 

def create(self):
        self.textual_embedding(self, mask_zero=True)
        self.stacked_RNN(self)
        self.add(self._config.recurrent_encoder(
            self._config.hidden_state_dim, 
            return_sequences=False,
            go_backwards=self._config.go_backwards))
        self.add(Dropout(0.5))
        self.add(RepeatVector(self._config.max_output_time_steps))
        self.add(self._config.recurrent_decoder(
                self._config.hidden_state_dim, return_sequences=True))
        self.add(Dropout(0.5))
        self.add(TimeDistributedDense(self._config.output_dim))
        self.add(Activation('softmax'))


###
# Multimodal models
### 

def create(self):
        assert self._config.merge_mode in ['max', 'ave', 'sum'], \
                'Merge mode of this model is either max, ave or sum'

        model_list = [None] * self._config.language_cnn_views
        for j in xrange(1,self._config.language_cnn_views+1):
            current_view = Sequential()
            self.textual_embedding(current_view, mask_zero=True)
            current_view.add(Convolution1D(
                nb_filter=self._config.language_cnn_filters,
                filter_length=j,
                border_mode='valid',
                activation=self._config.language_cnn_activation,
                subsample_length=1))
            self.temporal_pooling(current_view)
            model_list[j-1] = current_view

        self.add(Merge(model_list, mode='concat'))
        self.deep_mlp()
        self.add(Dense(self._config.output_dim))
        self.add(Activation('softmax')) 

def build_model(layers):
    model = Sequential()

    model.add(LSTM(
        input_dim=layers[0],
        output_dim=layers[1],
        return_sequences=True))
    model.add(Dropout(0.2))

    model.add(LSTM(
        layers[2],
        return_sequences=False))
    model.add(Dropout(0.2))

    model.add(Dense(
        output_dim=layers[2]))
    model.add(Activation("linear"))

    start = time.time()
    model.compile(loss="mse", optimizer="rmsprop", metrics=['accuracy'])
    print("Compilation Time : ", time.time() - start)
    return model 

def create(self):
        assert self._config.merge_mode in ['max', 'ave', 'sum'], \
                'Merge mode of this model is either max, ave or sum'

        self.textual_embedding(self, mask_zero=False)
        #self.textual_embedding(self, mask_zero=True)
        self.add(MaskedConvolution1D(
            nb_filter=self._config.language_cnn_filters,
            filter_length=self._config.language_cnn_filter_length,
            border_mode='valid',
            activation=self._config.language_cnn_activation,
            subsample_length=1))
        self.temporal_pooling(self)
        #self.add(DropMask())
        self.deep_mlp()
        self.add(Dense(self._config.output_dim))
        self.add(Activation('softmax')) 

def create(self):
        self.textual_embedding(self, mask_zero=False)
        self.add(Convolution1D(
            nb_filter=self._config.language_cnn_filters,
            filter_length=self._config.language_cnn_filter_length,
            border_mode='valid',
            activation=self._config.language_cnn_activation,
            subsample_length=1))
        #self.add(MaxPooling1D(pool_length=self._config.language_max_pool_length))
        self.add(self._config.recurrent_encoder(
            self._config.hidden_state_dim, 
            return_sequences=False,
            go_backwards=False))
        self.deep_mlp()
        self.add(Dense(self._config.output_dim))
        self.add(Activation('softmax')) 

def test_vector_clf(self):
        nb_hidden = 10

        print('vector classification data:')
        (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(10,),
                                                             classification=True, nb_class=2)
        print('X_train:', X_train.shape)
        print('X_test:', X_test.shape)
        print('y_train:', y_train.shape)
        print('y_test:', y_test.shape)

        y_train = to_categorical(y_train)
        y_test = to_categorical(y_test)

        model = Sequential()
        model.add(Dense(X_train.shape[-1], nb_hidden))
        model.add(Activation('relu'))
        model.add(Dense(nb_hidden, y_train.shape[-1]))
        model.add(Activation('softmax'))
        model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
        history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2)
        print(history.history)
        self.assertTrue(history.history['val_acc'][-1] > 0.9) 

def create(self):

        assert self._config.textual_embedding_dim == 0, \
                'Embedding cannot be learnt but must be fixed'

        language_forward = Sequential()
        language_forward.add(self._config.recurrent_encoder(
            self._config.hidden_state_dim, return_sequences=False,
            input_shape=(self._config.max_input_time_steps, self._config.input_dim)))
        self.language_forward = language_forward

        language_backward = Sequential()
        language_backward.add(self._config.recurrent_encoder(
            self._config.hidden_state_dim, return_sequences=False,
            go_backwards=True,
            input_shape=(self._config.max_input_time_steps, self._config.input_dim)))
        self.language_backward = language_backward

        self.add(Merge([language_forward, language_backward]))
        self.deep_mlp()
        self.add(Dense(self._config.output_dim))
        self.add(Activation('softmax')) 

def discriminator(img_dim,alpha=0.2):
    model = Sequential()
    model.add(
            Conv2D(64, kernel_size=5,strides=2,
            padding='same',
            input_shape=img_dim)
            )
    model.add(LeakyReLU(alpha))
    model.add(Conv2D(128,kernel_size=5,strides=2,padding='same'))
    model.add(BatchNormalization())
    model.add(LeakyReLU(alpha))
    model.add(Conv2D(256,kernel_size=5,strides=2,padding='same'))
    model.add(BatchNormalization())
    model.add(LeakyReLU(alpha))
    model.add(Flatten())
    model.add(Dense(1))
    model.add(Activation('sigmoid'))
    return model

# Define a combination of Generator and Discriminator 

def test_vector_reg(self):
        nb_hidden = 10
        print('vector regression data:')
        (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(10,), output_shape=(2,),
                                                             classification=False)
        print('X_train:', X_train.shape)
        print('X_test:', X_test.shape)
        print('y_train:', y_train.shape)
        print('y_test:', y_test.shape)

        model = Sequential()
        model.add(Dense(X_train.shape[-1], nb_hidden))
        model.add(Activation('tanh'))
        model.add(Dense(nb_hidden, y_train.shape[-1]))
        model.compile(loss='hinge', optimizer='adagrad')
        history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), verbose=2)
        self.assertTrue(history.history['val_loss'][-1] < 0.9) 

def deep_mlp(self):
        """
        Deep Multilayer Perceptrop.
        """
        if self._config.num_mlp_layers == 0:
            self.add(Dropout(0.5))
        else:
            for j in xrange(self._config.num_mlp_layers):
                self.add(Dense(self._config.mlp_hidden_dim))
                if self._config.mlp_activation == 'elu':
                    self.add(ELU())
                elif self._config.mlp_activation == 'leaky_relu':
                    self.add(LeakyReLU())
                elif self._config.mlp_activation == 'prelu':
                    self.add(PReLU())
                else:
                    self.add(Activation(self._config.mlp_activation))
                self.add(Dropout(0.5)) 

def test_img_clf(self):
        print('image classification data:')
        (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(3, 32, 32),
                                                             classification=True, nb_class=2)
        print('X_train:', X_train.shape)
        print('X_test:', X_test.shape)
        print('y_train:', y_train.shape)
        print('y_test:', y_test.shape)

        y_train = to_categorical(y_train)
        y_test = to_categorical(y_test)

        model = Sequential()
        model.add(Convolution2D(32, 3, 32, 32))
        model.add(Activation('sigmoid'))
        model.add(Flatten())
        model.add(Dense(32, y_test.shape[-1]))
        model.add(Activation('softmax'))
        model.compile(loss='categorical_crossentropy', optimizer='sgd')
        history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2)
        self.assertTrue(history.history['val_acc'][-1] > 0.9) 

def model(x_train, y_train, x_test, y_test):
    """Model providing function:

    Create Keras model with double curly brackets dropped-in as needed.
    Return value has to be a valid python dictionary with two customary keys:
        - loss: Specify a numeric evaluation metric to be minimized
        - status: Just use STATUS_OK and see hyperopt documentation if not feasible
    The last one is optional, though recommended, namely:
        - model: specify the model just created so that we can later use it again.
    """
    from keras.models import Sequential
    from keras.layers.core import Dense, Dropout, Activation
    from keras.optimizers import RMSprop

    keras_model = Sequential()
    keras_model.add(Dense(512, input_shape=(784,)))
    keras_model.add(Activation('relu'))
    keras_model.add(Dropout({{uniform(0, 1)}}))
    keras_model.add(Dense({{choice([256, 512, 1024])}}))
    keras_model.add(Activation('relu'))
    keras_model.add(Dropout({{uniform(0, 1)}}))
    keras_model.add(Dense(10))
    keras_model.add(Activation('softmax'))

    rms = RMSprop()
    keras_model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['acc'])

    keras_model.fit(x_train, y_train,
                    batch_size={{choice([64, 128])}},
                    epochs=1,
                    verbose=2,
                    validation_data=(x_test, y_test))
    score, acc = keras_model.evaluate(x_test, y_test, verbose=0)
    print('Test accuracy:', acc)
    return {'loss': -acc, 'status': STATUS_OK, 'model': keras_model.to_yaml(),
            'weights': pickle.dumps(keras_model.get_weights())}


# Create Spark context 

def model(self):
        # Input
        input_layer = Input(shape=self.SHAPE)
        x = Convolution2D(64, 3,3, border_mode='same',activation='relu')(input_layer)

        # ResNet Block 1
        res_x_input_1 = Conv2D(64, (3,3), border_mode='same',activation='relu')(x)
        x = Convolution2D(64, 3,3, border_mode='same',activation='relu')(res_x_input_1)
        x = layers.Add()([res_x_input_1,x])
        x = Activation('relu')(x)

        # ResNet Block 2
        res_x_input_2 = Conv2D(64, (3,3), border_mode='same',activation='relu')(x)
        x = Convolution2D(64, 3,3, border_mode='same',activation='relu')(res_x_input_2)
        x = layers.Add()([res_x_input_2,x])
        x = Activation('relu')(x)

        # ResNet Block 3
        res_x_input_3 = Conv2D(64, (3,3), border_mode='same',activation='relu')(x)
        x = Convolution2D(64, 3,3, border_mode='same',activation='relu')(res_x_input_3)
        x = layers.Add()([res_x_input_3,x])
        x = Activation('relu')(x)

        # ResNet Block 4
        res_x_input_4 = Conv2D(64, (3,3), border_mode='same',activation='relu')(x)
        x = Convolution2D(64, 3,3, border_mode='same',activation='relu')(res_x_input_4)
        x = layers.Add()([res_x_input_4,x])
        x = Activation('relu')(x)

        output_layer = Convolution2D(self.C,1,1, border_mode='same',activation='tanh')(x)
        
        return Model(input_layer,output_layer) 

def generator(input_dim,alpha=0.2):
    model = Sequential()
    model.add(Dense(input_dim=input_dim, output_dim=4*4*512))
    model.add(Reshape(target_shape=(4,4,512)))
    model.add(BatchNormalization())
    model.add(LeakyReLU(alpha))
    model.add(Conv2DTranspose(256, kernel_size=5, strides=2, padding='same'))
    model.add(BatchNormalization())
    model.add(LeakyReLU(alpha))
    model.add(Conv2DTranspose(128, kernel_size=5, strides=2, padding='same'))   
    model.add(BatchNormalization())
    model.add(LeakyReLU(alpha))
    model.add(Conv2DTranspose(3, kernel_size=5, strides=2, padding='same'))   
    model.add(Activation('tanh'))
    return model

#Define the Discriminator Network 

def performRNNlass(X_train, y_train):
    X_train = numpy.reshape(numpy.array(X_train), (X_train.shape[0], 1, X_train.shape[1]))
    model = Sequential()

    model.add(LSTM(
        128,
        input_shape=(None, X_train.shape[2]),
        return_sequences=True))
    model.add(Dropout(0.2))

    model.add(LSTM(
        100,
        return_sequences=False))
    model.add(Dropout(0.2))

    model.add(Dense(
        units=1))
    model.add(Activation('sigmoid'))

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

    model.fit(
        X_train,
        y_train,
        batch_size=64,
        epochs=64,
        validation_split=0.1)
    return model