Python keras.models.Sequential() Examples

The following are 30 code examples of keras.models.Sequential(). 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.models , 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: 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 #3
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 #4
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 #5
Source Project: Keras-GAN   Author: eriklindernoren   File: infogan.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(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) 
Example #6
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 #7
Source Project: Keras-GAN   Author: eriklindernoren   File: lsgan.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 #8
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 #9
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 #10
Source Project: Keras-GAN   Author: eriklindernoren   File: dualgan.py    License: MIT License 6 votes vote down vote up
def build_generator(self):

        X = Input(shape=(self.img_dim,))

        model = Sequential()
        model.add(Dense(256, input_dim=self.img_dim))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dropout(0.4))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dropout(0.4))
        model.add(Dense(1024))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dropout(0.4))
        model.add(Dense(self.img_dim, activation='tanh'))

        X_translated = model(X)

        return Model(X, X_translated) 
Example #11
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 #12
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 #13
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 #14
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 #15
Source Project: keras2pmml   Author: vaclavcadek   File: sequential.py    License: MIT License 6 votes vote down vote up
def setUp(self):
        iris = load_iris()

        theano.config.floatX = 'float32'
        X = iris.data.astype(theano.config.floatX)
        y = iris.target.astype(np.int32)
        y_ohe = np_utils.to_categorical(y)

        model = Sequential()
        model.add(Dense(input_dim=X.shape[1], output_dim=5, activation='tanh'))
        model.add(Dense(input_dim=5, output_dim=y_ohe.shape[1], activation='sigmoid'))
        model.compile(loss='categorical_crossentropy', optimizer='sgd')
        model.fit(X, y_ohe, nb_epoch=10, batch_size=1, verbose=3, validation_data=None)

        params = {'copyright': 'Václav Čadek', 'model_name': 'Iris Model'}
        self.model = model
        self.pmml = keras2pmml(self.model, **params)
        self.num_inputs = self.model.input_shape[1]
        self.num_outputs = self.model.output_shape[1]
        self.num_connection_layers = len(self.model.layers)
        self.features = ['x{}'.format(i) for i in range(self.num_inputs)]
        self.class_values = ['y{}'.format(i) for i in range(self.num_outputs)] 
Example #16
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 #17
Source Project: residual_block_keras   Author: keunwoochoi   File: example.py    License: GNU General Public License v3.0 6 votes vote down vote up
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 
Example #18
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 #19
Source Project: Jtyoui   Author: jtyoui   File: HandWritingRecognition.py    License: MIT License 6 votes vote down vote up
def nn_model():
    (x_train, y_train), _ = mnist.load_data()
    # 归一化
    x_train = x_train.reshape(x_train.shape[0], -1) / 255.
    # one-hot
    y_train = np_utils.to_categorical(y=y_train, num_classes=10)
    # constant(value=1.)自定义常数,constant(value=1.)===one()
    # 创建模型:输入784个神经元,输出10个神经元
    model = Sequential([
        Dense(units=200, input_dim=784, bias_initializer=constant(value=1.), activation=tanh),
        Dense(units=100, bias_initializer=one(), activation=tanh),
        Dense(units=10, bias_initializer=one(), activation=softmax),
    ])

    opt = SGD(lr=0.2, clipnorm=1.)  # 优化器
    model.compile(optimizer=opt, loss=categorical_crossentropy, metrics=['acc', 'mae'])  # 编译
    model.fit(x_train, y_train, batch_size=64, epochs=20, callbacks=[RemoteMonitor()])
    model_save(model, './model.h5') 
Example #20
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 #21
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 #22
Source Project: LearningX   Author: ankonzoid   File: NN_regr.py    License: MIT License 6 votes vote down vote up
def fit(self, X, y):
        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
        assert len(X.shape) == 2
        N, d = X.shape

        from keras.models import Sequential
        from keras.layers import Dense
        from keras.optimizers import Adam
        model = Sequential()
        model.add(Dense(10, input_dim=d, activation="relu"))
        model.add(Dense(10, activation="relu"))
        model.add(Dense(1, activation="relu"))
        model.compile(loss="mse", optimizer=Adam(lr=0.005))
        self.model = model

        n_epochs = 100
        self.model.fit(X, y, epochs=n_epochs, verbose=False) 
Example #23
Source Project: blackbox-attacks   Author: sunblaze-ucb   File: mnist.py    License: MIT License 6 votes vote down vote up
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 
Example #24
Source Project: blackbox-attacks   Author: sunblaze-ucb   File: mnist.py    License: MIT License 6 votes vote down vote up
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 
Example #25
Source Project: blackbox-attacks   Author: sunblaze-ucb   File: mnist.py    License: MIT License 6 votes vote down vote up
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 
Example #26
Source Project: blackbox-attacks   Author: sunblaze-ucb   File: mnist.py    License: MIT License 6 votes vote down vote up
def modelD():
    model = Sequential()

    model.add(Flatten(input_shape=(FLAGS.IMAGE_ROWS,
                                   FLAGS.IMAGE_COLS,
                                   FLAGS.NUM_CHANNELS)))

    model.add(Dense(300, init='he_normal', activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(300, init='he_normal', activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(300, init='he_normal', activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(300, init='he_normal', activation='relu'))
    model.add(Dropout(0.5))

    model.add(Dense(FLAGS.NUM_CLASSES))
    return model 
Example #27
Source Project: spark-deep-learning   Author: databricks   File: keras_transformer_test.py    License: Apache License 2.0 6 votes vote down vote up
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") 
Example #28
Source Project: spark-deep-learning   Author: databricks   File: keras_sql_udf_test.py    License: Apache License 2.0 6 votes vote down vote up
def test_simple_keras_udf(self):
        """ Simple Keras sequential model """
        # Notice that the input layer for a image UDF model
        # must be of shape (width, height, numChannels)
        # The leading batch size is taken care of by Keras
        with IsolatedSession(using_keras=True) as issn:
            model = Sequential()
            # Make the test model simpler to increase the stability of travis tests
            model.add(Flatten(input_shape=(640, 480, 3)))
            # model.add(Dense(64, activation='relu'))
            model.add(Dense(16, activation='softmax'))
            # Initialize the variables
            init_op = tf.global_variables_initializer()
            issn.run(init_op)
            makeGraphUDF(issn.graph,
                         'my_keras_model_udf',
                         model.outputs,
                         {tfx.op_name(model.inputs[0], issn.graph): 'image_col'})
            # Run the training procedure
            # Export the graph in this IsolatedSession as a GraphFunction
            # gfn = issn.asGraphFunction(model.inputs, model.outputs)
            fh_name = "test_keras_simple_sequential_model"
            registerKerasImageUDF(fh_name, model)

        self._assert_function_exists(fh_name) 
Example #29
Source Project: Python-Deep-Learning-SE   Author: ivan-vasilev   File: chapter_06_002.py    License: MIT License 6 votes vote down vote up
def build_discriminator():
    """
    Build discriminator network
    """

    model = Sequential([
        Flatten(input_shape=(28, 28, 1)),
        Dense(256),
        LeakyReLU(alpha=0.2),
        Dense(128),
        LeakyReLU(alpha=0.2),
        Dense(1, activation='sigmoid'),
    ], name='discriminator')

    model.summary()

    image = Input(shape=(28, 28, 1))
    output = model(image)

    return Model(image, output) 
Example #30
Source Project: Convolutional-Networks-for-Stock-Predicting   Author: JasonDoingGreat   File: cnn_main.py    License: MIT License 6 votes vote down vote up
def create_model():
    model = Sequential()

    model.add(Convolution2D(32, 3, 3,
                            border_mode='valid', 
                            input_shape=(100, 100, 3)))  
    model.add(Activation('relu'))  
    model.add(Convolution2D(32, 3, 3))  
    model.add(Activation('relu'))  
    model.add(MaxPooling2D(pool_size=(2, 2)))  
    model.add(Dropout(0.25))  
      
    model.add(Convolution2D(64, 3, 3, 
                            border_mode='valid'))  
    model.add(Activation('relu'))  
    model.add(Convolution2D(64, 3, 3))  
    model.add(Activation('relu'))  
    model.add(MaxPooling2D(pool_size=(2, 2)))  
    model.add(Dropout(0.25))  
      
    model.add(Flatten())  
    model.add(Dense(256))  
    model.add(Activation('relu'))  
    model.add(Dropout(0.5))

    model.add(Dense(2))  
    model.add(Activation('softmax'))  

    return model