Python keras.layers.Dense() Examples

The following are 30 code examples of keras.layers.Dense(). 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 , or try the search function .
Example #1
Source Project: keras-anomaly-detection   Author: chen0040   File: recurrent.py    License: MIT License 16 votes vote down vote up
def create_model(time_window_size, metric):
        model = Sequential()

        model.add(Conv1D(filters=256, kernel_size=5, padding='same', activation='relu',
                         input_shape=(time_window_size, 1)))
        model.add(MaxPooling1D(pool_size=4))

        model.add(LSTM(64))

        model.add(Dense(units=time_window_size, activation='linear'))

        model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric])

        # model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric])
        # model.compile(optimizer="sgd", loss="mse", metrics=[metric])

        print(model.summary())
        return model 
Example #2
Source Project: Image-Caption-Generator   Author: dabasajay   File: model.py    License: MIT License 11 votes vote down vote up
def RNNModel(vocab_size, max_len, rnnConfig, model_type):
	embedding_size = rnnConfig['embedding_size']
	if model_type == 'inceptionv3':
		# InceptionV3 outputs a 2048 dimensional vector for each image, which we'll feed to RNN Model
		image_input = Input(shape=(2048,))
	elif model_type == 'vgg16':
		# VGG16 outputs a 4096 dimensional vector for each image, which we'll feed to RNN Model
		image_input = Input(shape=(4096,))
	image_model_1 = Dropout(rnnConfig['dropout'])(image_input)
	image_model = Dense(embedding_size, activation='relu')(image_model_1)

	caption_input = Input(shape=(max_len,))
	# mask_zero: We zero pad inputs to the same length, the zero mask ignores those inputs. E.g. it is an efficiency.
	caption_model_1 = Embedding(vocab_size, embedding_size, mask_zero=True)(caption_input)
	caption_model_2 = Dropout(rnnConfig['dropout'])(caption_model_1)
	caption_model = LSTM(rnnConfig['LSTM_units'])(caption_model_2)

	# Merging the models and creating a softmax classifier
	final_model_1 = concatenate([image_model, caption_model])
	final_model_2 = Dense(rnnConfig['dense_units'], activation='relu')(final_model_1)
	final_model = Dense(vocab_size, activation='softmax')(final_model_2)

	model = Model(inputs=[image_input, caption_input], outputs=final_model)
	model.compile(loss='categorical_crossentropy', optimizer='adam')
	return model 
Example #3
Source Project: Keras-GAN   Author: eriklindernoren   File: sgan.py    License: MIT License 8 votes vote down vote up
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) 
Example #4
Source Project: vergeml   Author: mme   File: imagenet.py    License: MIT License 7 votes vote down vote up
def _makenet(x, num_layers, dropout, random_seed):
    from keras.layers import Dense, Dropout

    dropout_seeder = random.Random(random_seed)

    for i in range(num_layers - 1):
        # add intermediate layers
        if dropout:
            x = Dropout(dropout, seed=dropout_seeder.randint(0, 10000))(x)
        x = Dense(1024, activation="relu", name='dense_layer_{}'.format(i))(x)

    if dropout:
        # add the final dropout layer
        x = Dropout(dropout, seed=dropout_seeder.randint(0, 10000))(x)

    return x 
Example #5
Source Project: Deep_Learning_Weather_Forecasting   Author: BruceBinBoxing   File: weather_model.py    License: Apache License 2.0 7 votes vote down vote up
def weather_l2(hidden_nums=100,l2=0.01): 
    input_img = Input(shape=(37,))
    hn = Dense(hidden_nums, activation='relu')(input_img)
    hn = Dense(hidden_nums, activation='relu',
               kernel_regularizer=regularizers.l2(l2))(hn)
    out_u = Dense(37, activation='sigmoid',                 
                  name='ae_part')(hn)
    out_sig = Dense(37, activation='linear', 
                    name='pred_part')(hn)
    out_both = concatenate([out_u, out_sig], axis=1, name = 'concatenate')

    #weather_model = Model(input_img, outputs=[out_ae, out_pred])
    mve_model = Model(input_img, outputs=[out_both])
    mve_model.compile(optimizer='adam', loss=mve_loss, loss_weights=[1.])
    
    return mve_model 
Example #6
Source Project: Keras-GAN   Author: eriklindernoren   File: context_encoder.py    License: MIT License 7 votes vote down vote up
def build_discriminator(self):

        model = Sequential()

        model.add(Conv2D(64, kernel_size=3, strides=2, input_shape=self.missing_shape, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Conv2D(256, kernel_size=3, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Flatten())
        model.add(Dense(1, activation='sigmoid'))
        model.summary()

        img = Input(shape=self.missing_shape)
        validity = model(img)

        return Model(img, validity) 
Example #7
Source Project: Jtyoui   Author: jtyoui   File: cnn_rnn_crf.py    License: MIT License 7 votes vote down vote up
def create_model():
    inputs = Input(shape=(length,), dtype='int32', name='inputs')
    embedding_1 = Embedding(len(vocab), EMBED_DIM, input_length=length, mask_zero=True)(inputs)
    bilstm = Bidirectional(LSTM(EMBED_DIM // 2, return_sequences=True))(embedding_1)
    bilstm_dropout = Dropout(DROPOUT_RATE)(bilstm)
    embedding_2 = Embedding(len(vocab), EMBED_DIM, input_length=length)(inputs)
    con = Conv1D(filters=FILTERS, kernel_size=2 * HALF_WIN_SIZE + 1, padding='same')(embedding_2)
    con_d = Dropout(DROPOUT_RATE)(con)
    dense_con = TimeDistributed(Dense(DENSE_DIM))(con_d)
    rnn_cnn = concatenate([bilstm_dropout, dense_con], axis=2)
    dense = TimeDistributed(Dense(len(chunk_tags)))(rnn_cnn)
    crf = CRF(len(chunk_tags), sparse_target=True)
    crf_output = crf(dense)
    model = Model(input=[inputs], output=[crf_output])
    model.compile(loss=crf.loss_function, optimizer=Adam(), metrics=[crf.accuracy])
    return model 
Example #8
Source Project: keras-anomaly-detection   Author: chen0040   File: feedforward.py    License: MIT License 6 votes vote down vote up
def create_model(self, input_dim):
        encoding_dim = 14
        input_layer = Input(shape=(input_dim,))

        encoder = Dense(encoding_dim, activation="tanh",
                        activity_regularizer=regularizers.l1(10e-5))(input_layer)
        encoder = Dense(encoding_dim // 2, activation="relu")(encoder)

        decoder = Dense(encoding_dim // 2, activation='tanh')(encoder)
        decoder = Dense(input_dim, activation='relu')(decoder)

        model = Model(inputs=input_layer, outputs=decoder)
        model.compile(optimizer='adam',
                      loss='mean_squared_error',
                      metrics=['accuracy'])

        return model 
Example #9
Source Project: Deep_Learning_Weather_Forecasting   Author: BruceBinBoxing   File: weather_model.py    License: Apache License 2.0 6 votes vote down vote up
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 
Example #10
Source Project: Deep_Learning_Weather_Forecasting   Author: BruceBinBoxing   File: weather_model.py    License: Apache License 2.0 6 votes vote down vote up
def weather_ae(layers, lr, decay, loss, 
               input_len, input_features):
    
    inputs = Input(shape=(input_len, input_features), name='input_layer')
    
    for i, hidden_nums in enumerate(layers):
        if i==0:
            hn = Dense(hidden_nums, activation='relu')(inputs)
        else:
            hn = Dense(hidden_nums, activation='relu')(hn)

    outputs = Dense(3, activation='sigmoid', name='output_layer')(hn)

    weather_model = Model(inputs, outputs=[outputs])

    return weather_model 
Example #11
Source Project: cnn-levelset   Author: wiseodd   File: localizer.py    License: MIT License 6 votes vote down vote up
def __init__(self, model_path=None):
        if model_path is not None:
            self.model = self.load_model(model_path)
        else:
            # VGG16 last conv features
            inputs = Input(shape=(7, 7, 512))
            x = Convolution2D(128, 1, 1)(inputs)
            x = Flatten()(x)

            # Cls head
            h_cls = Dense(256, activation='relu', W_regularizer=l2(l=0.01))(x)
            h_cls = Dropout(p=0.5)(h_cls)
            cls_head = Dense(20, activation='softmax', name='cls')(h_cls)

            # Reg head
            h_reg = Dense(256, activation='relu', W_regularizer=l2(l=0.01))(x)
            h_reg = Dropout(p=0.5)(h_reg)
            reg_head = Dense(4, activation='linear', name='reg')(h_reg)

            # Joint model
            self.model = Model(input=inputs, output=[cls_head, reg_head]) 
Example #12
Source Project: Keras-GAN   Author: eriklindernoren   File: ccgan.py    License: MIT License 6 votes vote down vote up
def build_discriminator(self):

        img = Input(shape=self.img_shape)

        model = Sequential()
        model.add(Conv2D(64, kernel_size=4, strides=2, padding='same', input_shape=self.img_shape))
        model.add(LeakyReLU(alpha=0.8))
        model.add(Conv2D(128, kernel_size=4, strides=2, padding='same'))
        model.add(LeakyReLU(alpha=0.2))
        model.add(InstanceNormalization())
        model.add(Conv2D(256, kernel_size=4, strides=2, padding='same'))
        model.add(LeakyReLU(alpha=0.2))
        model.add(InstanceNormalization())

        model.summary()

        img = Input(shape=self.img_shape)
        features = model(img)

        validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(features)

        label = Flatten()(features)
        label = Dense(self.num_classes+1, activation="softmax")(label)

        return Model(img, [validity, label]) 
Example #13
Source Project: Keras-GAN   Author: eriklindernoren   File: bigan.py    License: MIT License 6 votes vote down vote up
def build_encoder(self):
        model = Sequential()

        model.add(Flatten(input_shape=self.img_shape))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(self.latent_dim))

        model.summary()

        img = Input(shape=self.img_shape)
        z = model(img)

        return Model(img, z) 
Example #14
Source Project: Keras-GAN   Author: eriklindernoren   File: bigan.py    License: MIT License 6 votes vote down vote up
def build_discriminator(self):

        z = Input(shape=(self.latent_dim, ))
        img = Input(shape=self.img_shape)
        d_in = concatenate([z, Flatten()(img)])

        model = Dense(1024)(d_in)
        model = LeakyReLU(alpha=0.2)(model)
        model = Dropout(0.5)(model)
        model = Dense(1024)(model)
        model = LeakyReLU(alpha=0.2)(model)
        model = Dropout(0.5)(model)
        model = Dense(1024)(model)
        model = LeakyReLU(alpha=0.2)(model)
        model = Dropout(0.5)(model)
        validity = Dense(1, activation="sigmoid")(model)

        return Model([z, img], validity) 
Example #15
Source Project: Keras-GAN   Author: eriklindernoren   File: pixelda.py    License: MIT License 6 votes vote down vote up
def build_classifier(self):

        def clf_layer(layer_input, filters, f_size=4, normalization=True):
            """Classifier layer"""
            d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
            d = LeakyReLU(alpha=0.2)(d)
            if normalization:
                d = InstanceNormalization()(d)
            return d

        img = Input(shape=self.img_shape)

        c1 = clf_layer(img, self.cf, normalization=False)
        c2 = clf_layer(c1, self.cf*2)
        c3 = clf_layer(c2, self.cf*4)
        c4 = clf_layer(c3, self.cf*8)
        c5 = clf_layer(c4, self.cf*8)

        class_pred = Dense(self.num_classes, activation='softmax')(Flatten()(c5))

        return Model(img, class_pred) 
Example #16
Source Project: Keras-GAN   Author: eriklindernoren   File: wgan.py    License: MIT License 6 votes vote down vote up
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) 
Example #17
Source Project: Keras-GAN   Author: eriklindernoren   File: wgan_gp.py    License: MIT License 6 votes vote down vote up
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) 
Example #18
Source Project: Keras-GAN   Author: eriklindernoren   File: lsgan.py    License: MIT License 6 votes vote down vote up
def build_discriminator(self):

        model = Sequential()

        model.add(Flatten(input_shape=self.img_shape))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(256))
        model.add(LeakyReLU(alpha=0.2))
        # (!!!) No softmax
        model.add(Dense(1))
        model.summary()

        img = Input(shape=self.img_shape)
        validity = model(img)

        return Model(img, validity) 
Example #19
Source Project: Keras-GAN   Author: eriklindernoren   File: cogan.py    License: MIT License 6 votes vote down vote up
def build_discriminators(self):

        img1 = Input(shape=self.img_shape)
        img2 = Input(shape=self.img_shape)

        # Shared discriminator layers
        model = Sequential()
        model.add(Flatten(input_shape=self.img_shape))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(256))
        model.add(LeakyReLU(alpha=0.2))

        img1_embedding = model(img1)
        img2_embedding = model(img2)

        # Discriminator 1
        validity1 = Dense(1, activation='sigmoid')(img1_embedding)
        # Discriminator 2
        validity2 = Dense(1, activation='sigmoid')(img2_embedding)

        return Model(img1, validity1), Model(img2, validity2) 
Example #20
Source Project: Keras-GAN   Author: eriklindernoren   File: dcgan.py    License: MIT License 6 votes vote down vote up
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) 
Example #21
Source Project: Keras-GAN   Author: eriklindernoren   File: gan.py    License: MIT License 6 votes vote down vote up
def build_generator(self):

        model = Sequential()

        model.add(Dense(256, input_dim=self.latent_dim))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(1024))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(np.prod(self.img_shape), activation='tanh'))
        model.add(Reshape(self.img_shape))

        model.summary()

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

        return Model(noise, img) 
Example #22
Source Project: Keras-GAN   Author: eriklindernoren   File: gan.py    License: MIT License 6 votes vote down vote up
def build_discriminator(self):

        model = Sequential()

        model.add(Flatten(input_shape=self.img_shape))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(256))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(1, activation='sigmoid'))
        model.summary()

        img = Input(shape=self.img_shape)
        validity = model(img)

        return Model(img, validity) 
Example #23
Source Project: Keras-GAN   Author: eriklindernoren   File: aae.py    License: MIT License 6 votes vote down vote up
def build_decoder(self):

        model = Sequential()

        model.add(Dense(512, input_dim=self.latent_dim))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(np.prod(self.img_shape), activation='tanh'))
        model.add(Reshape(self.img_shape))

        model.summary()

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

        return Model(z, img) 
Example #24
Source Project: Keras-GAN   Author: eriklindernoren   File: bgan.py    License: MIT License 6 votes vote down vote up
def build_generator(self):

        model = Sequential()

        model.add(Dense(256, input_dim=self.latent_dim))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(1024))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(np.prod(self.img_shape), activation='tanh'))
        model.add(Reshape(self.img_shape))

        model.summary()

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

        return Model(noise, img) 
Example #25
Source Project: Keras-GAN   Author: eriklindernoren   File: bgan.py    License: MIT License 6 votes vote down vote up
def build_discriminator(self):

        model = Sequential()

        model.add(Flatten(input_shape=self.img_shape))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(256))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(1, activation='sigmoid'))
        model.summary()

        img = Input(shape=self.img_shape)
        validity = model(img)

        return Model(img, validity) 
Example #26
Source Project: tartarus   Author: sergiooramas   File: models.py    License: MIT License 6 votes vote down vote up
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 
Example #27
Source Project: Jtyoui   Author: jtyoui   File: NER.py    License: MIT License 6 votes vote down vote up
def train_model():
    if cxl_model:
        embedding_matrix = load_embedding()
    else:
        embedding_matrix = {}
    train, label = vocab_train_label(train_path, vocab=vocab, tags=tag, max_chunk_length=length)
    n = np.array(label, dtype=np.float)
    labels = n.reshape((n.shape[0], n.shape[1], 1))
    model = Sequential([
        Embedding(input_dim=len(vocab), output_dim=300, mask_zero=True, input_length=length, weights=[embedding_matrix],
                  trainable=False),
        SpatialDropout1D(0.2),
        Bidirectional(layer=LSTM(units=150, return_sequences=True, dropout=0.2, recurrent_dropout=0.2)),
        TimeDistributed(Dense(len(tag), activation=relu)),
    ])
    crf_ = CRF(units=len(tag), sparse_target=True)
    model.add(crf_)
    model.compile(optimizer=Adam(), loss=crf_.loss_function, metrics=[crf_.accuracy])
    model.fit(x=np.array(train), y=labels, batch_size=16, epochs=4, callbacks=[RemoteMonitor()])
    model.save(model_path) 
Example #28
Source Project: armchair-expert   Author: csvance   File: reaction.py    License: MIT License 6 votes vote down vote up
def __init__(self, path: str = None, use_gpu=False):

        import tensorflow as tf
        from keras.models import Sequential
        from keras.layers import Dense
        from keras.backend import set_session

        self.model = Sequential()
        self.model.add(Dense(AOLReactionFeatureAnalyzer.NUM_FEATURES, activation='relu',
                             input_dim=AOLReactionFeatureAnalyzer.NUM_FEATURES))
        self.model.add(Dense(AOLReactionFeatureAnalyzer.NUM_FEATURES - 2, activation='relu'))
        self.model.add(Dense(1, activation='sigmoid'))
        self.model.compile(optimizer='rmsprop',
                           loss='binary_crossentropy',
                           metrics=['accuracy'])

        if use_gpu:
            config = tf.ConfigProto()
            config.gpu_options.allow_growth = True
            set_session(tf.Session(config=config)) 
Example #29
Source Project: armchair-expert   Author: csvance   File: structure.py    License: MIT License 6 votes vote down vote up
def __init__(self, use_gpu: bool = False):
        import tensorflow as tf
        from keras.models import Sequential
        from keras.layers import Dense, Embedding
        from keras.layers import LSTM
        from keras.backend import set_session

        latent_dim = StructureModel.SEQUENCE_LENGTH * 8

        model = Sequential()
        model.add(
            Embedding(StructureFeatureAnalyzer.NUM_FEATURES, StructureFeatureAnalyzer.NUM_FEATURES,
                      input_length=StructureModel.SEQUENCE_LENGTH))
        model.add(LSTM(latent_dim, dropout=0.2, return_sequences=False))
        model.add(Dense(StructureFeatureAnalyzer.NUM_FEATURES, activation='softmax'))
        model.summary()
        model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
        self.model = model

        if use_gpu:
            config = tf.ConfigProto()
            config.gpu_options.allow_growth = True
            set_session(tf.Session(config=config)) 
Example #30
Source Project: Keras-BiGAN   Author: manicman1999   File: bigan.py    License: MIT License 6 votes vote down vote up
def encoder(self):

        if self.E:
            return self.E

        inp = Input(shape = [im_size, im_size, 3])

        x = d_block(inp, 1 * cha)   #64
        x = d_block(x, 2 * cha)   #32
        x = d_block(x, 3 * cha)   #16
        x = d_block(x, 4 * cha)  #8
        x = d_block(x, 8 * cha)  #4
        x = d_block(x, 16 * cha, p = False)  #4

        x = Flatten()(x)

        x = Dense(16 * cha, kernel_initializer = 'he_normal')(x)
        x = LeakyReLU(0.2)(x)

        x = Dense(latent_size, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(x)

        self.E = Model(inputs = inp, outputs = x)

        return self.E