本文整理匯總了Python中keras.models.Sequential方法的典型用法代碼示例。如果您正苦於以下問題:Python models.Sequential方法的具體用法?Python models.Sequential怎麽用?Python models.Sequential使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類keras.models
的用法示例。
在下文中一共展示了models.Sequential方法的15個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: create_model
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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
示例2: build_generator
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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)
示例3: build_discriminator
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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)
示例4: build_discriminator
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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])
示例5: build_generator
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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)
示例6: build_generator
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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)
示例7: build_generator
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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)
示例8: build_generator
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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)
示例9: build_discriminator
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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)
示例10: build_discriminators
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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)
示例11: build_generator
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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)
示例12: build_discriminator
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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)
示例13: build_decoder
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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)
示例14: setUp
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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)]
示例15: get_model_41
# 需要導入模塊: from keras import models [as 別名]
# 或者: from keras.models import Sequential [as 別名]
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