Python models.Sequential方法代码示例

# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import Sequential [as 别名]
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 

# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import Sequential [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 models [as 别名]
# 或者: from keras.models import Sequential [as 别名]
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) 

# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import Sequential [as 别名]
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]) 

# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import Sequential [as 别名]
def build_generator(self):
        model = Sequential()

        model.add(Dense(512, 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(np.prod(self.img_shape), activation='tanh'))
        model.add(Reshape(self.img_shape))

        model.summary()

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

        return Model(z, gen_img) 

# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import Sequential [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 models [as 别名]
# 或者: from keras.models import Sequential [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 models [as 别名]
# 或者: from keras.models import Sequential [as 别名]
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) 

# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import Sequential [as 别名]
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) 

# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import Sequential [as 别名]
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) 

# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import Sequential [as 别名]
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) 

# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import Sequential [as 别名]
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) 

# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import Sequential [as 别名]
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) 

# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import Sequential [as 别名]
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)] 

# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import Sequential [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