Python layers.Activation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 Activation [as 别名]
def _get_logits_name(self):
        """
        Looks for the name of the layer producing the logits.
        :return: name of layer producing the logits
        """
        softmax_name = self._get_softmax_name()
        softmax_layer = self.model.get_layer(softmax_name)

        if not isinstance(softmax_layer, Activation):
            # In this case, the activation is part of another layer
            return softmax_name

        if hasattr(softmax_layer, 'inbound_nodes'):
            warnings.warn(
                "Please update your version to keras >= 2.1.3; "
                "support for earlier keras versions will be dropped on "
                "2018-07-22")
            node = softmax_layer.inbound_nodes[0]
        else:
            node = softmax_layer._inbound_nodes[0]

        logits_name = node.inbound_layers[0].name

        return logits_name 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def CausalCNN(n_filters, lr, decay, loss, 
               seq_len, input_features, 
               strides_len, kernel_size,
               dilation_rates):

    inputs = Input(shape=(seq_len, input_features), name='input_layer')   
    x=inputs
    for dilation_rate in dilation_rates:
        x = Conv1D(filters=n_filters,
               kernel_size=kernel_size, 
               padding='causal',
               dilation_rate=dilation_rate,
               activation='linear')(x) 
        x = BatchNormalization()(x)
        x = Activation('relu')(x)

    #x = Dense(7, activation='relu', name='dense_layer')(x)
    outputs = Dense(3, activation='sigmoid', name='output_layer')(x)
    causalcnn = Model(inputs, outputs=[outputs])

    return causalcnn 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def build_generator(self):

        model = Sequential()

        model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
        model.add(Reshape((7, 7, 128)))
        model.add(BatchNormalization(momentum=0.8))
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=3, padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=3, padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Conv2D(1, kernel_size=3, padding="same"))
        model.add(Activation("tanh"))

        model.summary()

        noise = Input(shape=(self.latent_dim,))
        img = model(noise)

        return Model(noise, img) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def build_generator(self):

        model = Sequential()

        model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
        model.add(Reshape((7, 7, 128)))
        model.add(BatchNormalization(momentum=0.8))
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=3, padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=3, padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Conv2D(self.channels, kernel_size=3, padding='same'))
        model.add(Activation("tanh"))

        gen_input = Input(shape=(self.latent_dim,))
        img = model(gen_input)

        model.summary()

        return Model(gen_input, img) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def build_generator(self):

        model = Sequential()

        model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
        model.add(Reshape((7, 7, 128)))
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=4, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=4, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(Conv2D(self.channels, kernel_size=4, padding="same"))
        model.add(Activation("tanh"))

        model.summary()

        noise = Input(shape=(self.latent_dim,))
        img = model(noise)

        return Model(noise, img) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def build_generator(self):

        model = Sequential()

        model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
        model.add(Reshape((7, 7, 128)))
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
        model.add(Activation("tanh"))

        model.summary()

        noise = Input(shape=(self.latent_dim,))
        img = model(noise)

        return Model(noise, img) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def get_model_41(params):
    embedding_weights = pickle.load(open("../data/datasets/train_data/embedding_weights_w2v-google_MSD-AG.pk","rb"))
    # main sequential model
    model = Sequential()
    model.add(Embedding(len(embedding_weights[0]), params['embedding_dim'], input_length=params['sequence_length'],
                        weights=embedding_weights))
    #model.add(Dropout(params['dropout_prob'][0], input_shape=(params['sequence_length'], params['embedding_dim'])))
    model.add(LSTM(2048))
    #model.add(Dropout(params['dropout_prob'][1]))
    model.add(Dense(output_dim=params["n_out"], init="uniform"))
    model.add(Activation(params['final_activation']))
    logging.debug("Output CNN: %s" % str(model.output_shape))

    if params['final_activation'] == 'linear':
        model.add(Lambda(lambda x :K.l2_normalize(x, axis=1)))

    return model


# CRNN Arch for audio 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def g_block(inp, fil, u = True):

    if u:
        out = UpSampling2D(interpolation = 'bilinear')(inp)
    else:
        out = Activation('linear')(inp)

    skip = Conv2D(fil, 1, padding = 'same', kernel_initializer = 'he_normal')(out)

    out = Conv2D(filters = fil, kernel_size = 3, padding = 'same', kernel_initializer = 'he_normal')(out)
    out = LeakyReLU(0.2)(out)

    out = Conv2D(filters = fil, kernel_size = 3, padding = 'same', kernel_initializer = 'he_normal')(out)
    out = LeakyReLU(0.2)(out)

    out = Conv2D(fil, 1, padding = 'same', kernel_initializer = 'he_normal')(out)

    out = add([out, skip])
    out = LeakyReLU(0.2)(out)

    return out 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def nonlinearity(h_nonlin_name):

    def compile_fn(di, dh):

        def fn(di):
            nonlin_name = dh['nonlin_name']
            if nonlin_name == 'relu':
                Out = Activation('relu')(di['in'])
            elif nonlin_name == 'tanh':
                Out = Activation('tanh')(di['in'])
            elif nonlin_name == 'elu':
                Out = Activation('elu')(di['in'])
            else:
                raise ValueError
            return {"out": Out}

        return fn

    return hke.siso_keras_module('Nonlinearity', compile_fn,
                                 {'nonlin_name': h_nonlin_name}) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def evaluate(self, inputs, outputs):
        keras.backend.clear_session()

        X = Input(self.X_train[0].shape)
        co.forward({inputs['in']: X})
        logits = outputs['out'].val
        probs = Activation('softmax')(logits)

        model = Model(inputs=[inputs['in'].val], outputs=[probs])
        model.compile(optimizer=Adam(lr=self.learning_rate),
                      loss='sparse_categorical_crossentropy',
                      metrics=['accuracy'])
        model.summary()
        history = model.fit(self.X_train,
                            self.y_train,
                            batch_size=self.batch_size,
                            epochs=self.num_training_epochs,
                            validation_data=(self.X_val, self.y_val))
        results = {'validation_accuracy': history.history['val_accuracy'][-1]}
        return results 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def modelA():
    model = Sequential()
    model.add(Conv2D(64, (5, 5),
                            padding='valid'))
    model.add(Activation('relu'))

    model.add(Conv2D(64, (5, 5)))
    model.add(Activation('relu'))

    model.add(Dropout(0.25))

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

    model.add(Dropout(0.5))
    model.add(Dense(FLAGS.NUM_CLASSES))
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def modelB():
    model = Sequential()
    model.add(Dropout(0.2, input_shape=(FLAGS.IMAGE_ROWS,
                                        FLAGS.IMAGE_COLS,
                                        FLAGS.NUM_CHANNELS)))
    model.add(Convolution2D(64, 8, 8,
                            subsample=(2, 2),
                            border_mode='same'))
    model.add(Activation('relu'))

    model.add(Convolution2D(128, 6, 6,
                            subsample=(2, 2),
                            border_mode='valid'))
    model.add(Activation('relu'))

    model.add(Convolution2D(128, 5, 5,
                            subsample=(1, 1)))
    model.add(Activation('relu'))

    model.add(Dropout(0.5))

    model.add(Flatten())
    model.add(Dense(FLAGS.NUM_CLASSES))
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def modelC():
    model = Sequential()
    model.add(Convolution2D(128, 3, 3,
                            border_mode='valid',
                            input_shape=(FLAGS.IMAGE_ROWS,
                                         FLAGS.IMAGE_COLS,
                                         FLAGS.NUM_CHANNELS)))
    model.add(Activation('relu'))

    model.add(Convolution2D(64, 3, 3))
    model.add(Activation('relu'))

    model.add(Dropout(0.25))

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

    model.add(Dropout(0.5))
    model.add(Dense(FLAGS.NUM_CLASSES))
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def modelF():
    model = Sequential()

    model.add(Convolution2D(32, 3, 3,
                            border_mode='valid',
                            input_shape=(FLAGS.IMAGE_ROWS,
                                         FLAGS.IMAGE_COLS,
                                         FLAGS.NUM_CHANNELS)))
    model.add(Activation('relu'))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Convolution2D(64, 3, 3))
    model.add(Activation('relu'))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Flatten())
    model.add(Dense(1024))
    model.add(Activation('relu'))

    model.add(Dense(FLAGS.NUM_CLASSES))

    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Activation [as 别名]
def test_keras_transformer_single_dim(self):
        """
        Test that KerasTransformer correctly handles single-dimensional input data.
        """
        # Construct a model for simple binary classification (with a single hidden layer)
        model = Sequential()
        input_shape = [10]
        model.add(Dense(units=10, input_shape=input_shape,
                        bias_initializer=self._getKerasModelWeightInitializer(),
                        kernel_initializer=self._getKerasModelWeightInitializer()))
        model.add(Activation('relu'))
        model.add(Dense(units=1, bias_initializer=self._getKerasModelWeightInitializer(),
                        kernel_initializer=self._getKerasModelWeightInitializer()))
        model.add(Activation('sigmoid'))
        # Compare KerasTransformer output to raw Keras model output
        self._test_keras_transformer_helper(model, model_filename="keras_transformer_single_dim")