Python keras.models.Sequential() Examples
The following are 30
code examples of keras.models.Sequential().
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Example #1
| Source File: recurrent.py From keras-anomaly-detection with MIT License | 18 votes |
|
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 File: sgan.py From Keras-GAN with MIT License | 8 votes |
|
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 File: context_encoder.py From Keras-GAN with MIT License | 7 votes |
|
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 File: cogan.py From Keras-GAN with MIT License | 7 votes |
|
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 #5
| Source File: example.py From residual_block_keras with GNU General Public License v3.0 | 6 votes |
|
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 #6
| Source File: mnist.py From blackbox-attacks with MIT License | 6 votes |
|
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 #7
| Source File: bgan.py From Keras-GAN with MIT License | 6 votes |
|
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 #8
| Source File: HandWritingRecognition.py From Jtyoui with MIT License | 6 votes |
|
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 #9
| Source File: BuildModel.py From HDLTex with MIT License | 6 votes |
|
def buildModel_DNN(Shape, nClasses, nLayers=3,Number_Node=100, dropout=0.5):
'''
buildModel_DNN(nFeatures, nClasses, nLayers=3,Numberof_NOde=100, dropout=0.5)
Build Deep neural networks (Multi-layer perceptron) Model for text classification
Shape is input feature space
nClasses is number of classes
nLayers is number of hidden Layer
Number_Node is number of unit in each hidden layer
dropout is dropout value for solving overfitting problem
'''
model = Sequential()
model.add(Dense(Number_Node, input_dim=Shape))
model.add(Dropout(dropout))
for i in range(0,nLayers):
model.add(Dense(Number_Node, activation='relu'))
model.add(Dropout(dropout))
model.add(Dense(nClasses, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='RMSprop',
metrics=['accuracy'])
return model
Example #10
| Source File: cnn_main.py From Convolutional-Networks-for-Stock-Predicting with MIT License | 6 votes |
|
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
Example #11
| Source File: chapter_06_002.py From Python-Deep-Learning-SE with MIT License | 6 votes |
|
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 #12
| Source File: NER.py From Jtyoui with MIT License | 6 votes |
|
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 #13
| Source File: reaction.py From armchair-expert with MIT License | 6 votes |
|
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 #14
| Source File: sequential.py From keras2pmml with MIT License | 6 votes |
|
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 #15
| Source File: structure.py From armchair-expert with MIT License | 6 votes |
|
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 #16
| Source File: ccgan.py From Keras-GAN with MIT License | 6 votes |
|
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 #17
| Source File: mnist.py From blackbox-attacks with MIT License | 6 votes |
|
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 #18
| Source File: NN_regr.py From LearningX with MIT License | 6 votes |
|
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 #19
| Source File: wgan_gp.py From Keras-GAN with MIT License | 6 votes |
|
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 #20
| Source File: infogan.py From Keras-GAN with MIT License | 6 votes |
|
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 #21
| Source File: lsgan.py From Keras-GAN with MIT License | 6 votes |
|
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 File: lsgan.py From Keras-GAN with MIT License | 6 votes |
|
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 #23
| Source File: mnist.py From blackbox-attacks with MIT License | 6 votes |
|
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 #24
| Source File: keras_sql_udf_test.py From spark-deep-learning with Apache License 2.0 | 6 votes |
|
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 #25
| Source File: dualgan.py From Keras-GAN with MIT License | 6 votes |
|
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 #26
| Source File: mnist.py From blackbox-attacks with MIT License | 6 votes |
|
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 #27
| Source File: keras_transformer_test.py From spark-deep-learning with Apache License 2.0 | 6 votes |
|
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 File: gan.py From Keras-GAN with MIT License | 6 votes |
|
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 #29
| Source File: gan.py From Keras-GAN with MIT License | 6 votes |
|
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 #30
| Source File: aae.py From Keras-GAN with MIT License | 6 votes |
|
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)
