def __init__(self, restore = None, session=None, use_softmax=False):
self.num_channels = 1
self.image_size = 28
self.num_labels = 10
self.shape = [None, 28, 28, self.num_channels]
model = Sequential()
kernel_size = (5, 5)
drop_rate = 0.3
model.add(Conv2D(32, kernel_size, activation='relu',
padding='same', name='block1_conv1', input_shape=(28,28,1))) # 1
model.add(MaxPooling2D(pool_size=(2, 2), name='block1_pool1')) # 2
model.add(Dropout(drop_rate))
# block2
model.add(Conv2D(64, kernel_size, activation='relu', padding='same', name='block2_conv1')) # 4
model.add(MaxPooling2D(pool_size=(2, 2), name='block2_pool1')) # 5
model.add(Dropout(drop_rate))
model.add(Flatten(name='flatten'))
model.add(Dense(120, activation='relu', name='fc1')) # -5
model.add(Dropout(drop_rate))
model.add(Dense(84, activation='relu', name='fc2')) # -3
model.add(Dense(10, name='before_softmax')) # -2
model.add(Activation('softmax', name='predictions')) #
if restore:
model.load_weights(restore)
layer_outputs = []
for layer in model.layers:
if isinstance(layer, Conv2D) or isinstance(layer, Dense):
layer_outputs.append(K.function([model.layers[0].input], [layer.output]))
self.layer_outputs = layer_outputs
self.model = model
def build_regressor():
regressor = Sequential()
regressor.add(Dense(units=6, kernel_initializer='uniform', activation='relu', input_dim=11))
regressor.add(Dense(units=6, kernel_initializer='uniform', activation='relu'))
regressor.add(Dense(units=1, kernel_initializer='uniform', activation='linear'))
regressor.compile(optimizer='adam', loss='mean_squared_error')
return regressor
def network(num_classes):
model = Sequential()
model.add(Dense(10, activation='relu', input_shape=(4, )))
model.add(Dense(20, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
return model
def build_generator_dense():
model = Sequential()
# Add arbitrary layers
first = True
for size in generator_layers.split(":"):
size = int(size)
if first:
model.add(Dense(size, input_shape=noise_shape, activation=generator_activation))
else:
model.add(Dense(size, activation=generator_activation))
model.add(Dropout(dropout_value))
first = False
# Add the final layer
model.add(Dense( np.prod(url_shape) , activation="tanh"))
model.add(Dropout(dropout_value))
model.add(Reshape(url_shape))
model.summary()
# Build the model
noise = Input(shape=noise_shape)
gen = model(noise)
return Model(noise, gen)
def __init__(self, restore=None, session=None, use_softmax=False):
self.num_channels = 1
self.image_size = 28
self.num_labels = 10
self.shape = [None, 28 * 28]
model = Sequential()
model.add(
Dense(512,
activation='relu',
input_shape=(28 * 28, ),
name='dense_1'))
model.add(Dropout(0.2, name='d1'))
model.add(Dense(512, activation='relu', name='dense_2'))
model.add(Dropout(0.2, name='d2'))
model.add(Dense(10, activation='softmax', name='dense_3'))
if restore:
model.load_weights(restore, by_name=True)
layer_outputs = []
for layer in model.layers:
if isinstance(layer, Conv2D) or isinstance(layer, Dense):
layer_outputs.append(
K.function([model.layers[0].input], [layer.output]))
self.layer_outputs = layer_outputs
self.model = model
def latent(data_shape):
model = Sequential()
model.add(Dense(mc._OUT_DIM, activation='relu', input_shape=(data_shape,),\
kernel_regularizer=regularizers.l2(mc._L2_REGULARIZE_RATE)))
model.add(Dense(mc._OUT_DIM, activation='relu', input_shape=(data_shape,),\
kernel_regularizer=regularizers.l2(mc._L2_REGULARIZE_RATE)))
return model
def __init__(self, restore = None, session=None, use_softmax=False):
self.num_channels = 1
self.image_size = 28
self.num_labels = 10
self.shape = [None, 28, 28, self.num_channels]
model = Sequential()
model.add(Conv2D(6, (5, 5), padding='valid', activation='relu', kernel_initializer='he_normal',
input_shape=(28,28,1), name='l1'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='l2'))
model.add(Conv2D(16, (5, 5), padding='valid', activation='relu', kernel_initializer='he_normal', name='l3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='l4'))
model.add(Flatten())
model.add(Dense(120, activation='relu', kernel_initializer='he_normal', name='l5'))
model.add(Dense(84, activation='relu', kernel_initializer='he_normal', name='l6'))
model.add(Dense(10, activation='softmax', kernel_initializer='he_normal', name='l7'))
if restore:
model.load_weights(restore)
layer_outputs = []
for layer in model.layers:
if isinstance(layer, Conv2D) or isinstance(layer, Dense):
layer_outputs.append(K.function([model.layers[0].input], [layer.output]))
self.layer_outputs = layer_outputs
self.model = model
def __init__(self, restore=None, session=None, use_softmax=False):
self.num_channels = 3
self.image_size = 32
self.num_labels = 10
model = Sequential()
model.add(Conv2D(64, (3, 3), input_shape=(32, 32, 3)))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dense(10))
if use_softmax:
model.add(Activation('softmax'))
if restore:
model.load_weights(restore)
self.model = model
def __init__(self, restore=None, session=None, use_log=False):
self.num_channels = 1
self.image_size = 28
self.num_labels = 10
model = Sequential()
model.add(Flatten(input_shape=(28, 28, 1)))
model.add(Dense(1024))
model.add(Lambda(lambda x: x * 10))
model.add(Activation('softplus'))
model.add(Lambda(lambda x: x * 0.1))
model.add(Dense(10))
# output log probability, used for black-box attack
if use_log:
model.add(Activation('softmax'))
if restore:
model.load_weights(restore)
layer_outputs = []
for layer in model.layers:
if isinstance(layer, Conv2D) or isinstance(layer, Dense):
layer_outputs.append(
K.function([model.layers[0].input], [layer.output]))
self.layer_outputs = layer_outputs
self.model = model
def build_local_model(self):
input = Input(shape=self.state_size)
conv = TimeDistributed(
Conv2D(32, (8, 8), strides=(4, 4), activation="elu"))(input)
conv = TimeDistributed(
Conv2D(32, (4, 4), strides=(2, 2), activation="elu"))(conv)
conv = TimeDistributed(
Conv2D(32, (3, 3), strides=(1, 1), activation='elu'))(conv)
conv = TimeDistributed(
Conv2D(8, (1, 1), strides=(1, 1), activation='elu'))(conv)
conv = TimeDistributed(Flatten())(conv)
conv = BatchNormalization()(conv)
lstm = GRU(256, activation='tanh')(conv)
policy = Dense(self.action_size, activation="softmax")(lstm)
value = Dense(1, activation='linear')(lstm)
local_actor = Model(inputs=input, outputs=policy)
local_critic = Model(inputs=input, outputs=value)
local_actor._make_predict_function()
local_critic._make_predict_function()
local_actor.set_weights(self.actor.get_weights())
local_critic.set_weights(self.critic.get_weights())
return local_actor, local_critic
def __init__(self, params, restore = None, session=None, use_log=False, image_size=28, image_channel=1):
self.image_size = image_size
self.num_channels = image_channel
self.num_labels = 10
model = Sequential()
model.add(Flatten(input_shape=(image_size, image_size, image_channel)))
# list of all hidden units weights
self.U = []
for param in params:
# add each dense layer, and save a reference to list U
self.U.append(Dense(param))
model.add(self.U[-1])
# ReLU activation
model.add(Activation('relu'))
self.W = Dense(10)
model.add(self.W)
# output log probability, used for black-box attack
if use_log:
model.add(Activation('softmax'))
if restore:
model.load_weights(restore)
layer_outputs = []
for layer in model.layers:
if isinstance(layer, Conv2D) or isinstance(layer, Dense):
layer_outputs.append(K.function([model.layers[0].input], [layer.output]))
self.layer_outputs = layer_outputs
self.model = model
def conv_3d(self):
"""
Build a 3D convolutional network, based loosely on C3D.
https://arxiv.org/pdf/1412.0767.pdf
"""
# Model.
model = Sequential()
model.add(
Conv3D(32, (3, 3, 3),
activation='relu',
input_shape=self.input_shape))
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2)))
model.add(Conv3D(64, (3, 3, 3), activation='relu'))
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2)))
model.add(Conv3D(128, (3, 3, 3), activation='relu'))
model.add(Conv3D(128, (3, 3, 3), activation='relu'))
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2)))
model.add(Conv3D(256, (2, 2, 2), activation='relu'))
model.add(Conv3D(256, (2, 2, 2), activation='relu'))
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2)))
model.add(Flatten())
model.add(Dense(1024))
model.add(Dropout(0.5))
model.add(Dense(1024))
model.add(Dropout(0.5))
model.add(Dense(self.nb_classes, activation='softmax'))
return model
def __init__(self, nn_type="resnet50", restore = None, session=None, use_imagenet_pretrain=False, use_softmax=True):
self.image_size = 224
self.num_channels = 3
self.num_labels = 8
input_layer = Input(shape=(self.image_size, self.image_size, self.num_channels))
weights = "imagenet" if use_imagenet_pretrain else None
if nn_type == "resnet50":
base_model = ResNet50(weights=weights, input_tensor=input_layer)
elif nn_type == "vgg16":
base_model = VGG16(weights=weights, input_tensor=input_layer)
# base_model = VGG16(weights=None, input_tensor=input_layer)
x = base_model.output
x = LeakyReLU()(x)
x = Dense(1024)(x)
x = Dropout(0.2)(x)
x = LeakyReLU()(x)
x = Dropout(0.3)(x)
x = Dense(8)(x)
if use_softmax:
x = Activation("softmax")(x)
model = Model(inputs=base_model.input, outputs=x)
# for layer in base_model.layers:
# layer.trainable = False
if restore:
print("Load: {}".format(restore))
model.load_weights(restore)
self.model = model
def build_classifier():
classifier = Sequential()
classifier.add(Dense(units=6, kernel_initializer='uniform', activation='relu', input_dim=11))
classifier.add(Dense(units=6, kernel_initializer='uniform', activation='relu'))
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return classifier
def get_clf():
"""
Standard neural network training procedure.
"""
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(28, 28, 1)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(200))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(200))
model.add(Activation('relu'))
model.add(Dense(10))
model.load_weights("./models/mnist")
def fn(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels=correct, logits=predicted)
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss=fn, optimizer=sgd, metrics=['accuracy'])
return model
def __init__(self, restore=None, session=None, use_softmax=False):
self.num_channels = 1
self.image_size = 28
self.num_labels = 10
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(28, 28, 1)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(200))
model.add(Activation('relu'))
model.add(Dense(200))
model.add(Activation('relu'))
model.add(Dense(10))
# output log probability, used for black-box attack
if use_softmax:
model.add(Activation('softmax'))
if restore:
model.load_weights(restore)
self.model = model
def train(data,
file_name,
params,
num_epochs=50,
batch_size=128,
train_temp=1,
init=None,
lr=0.01,
decay=1e-5,
momentum=0.9):
"""
Train a n-layer simple network for MNIST and CIFAR
"""
# create a Keras sequential model
model = Sequential()
# reshape the input (28*28*1) or (32*32*3) to 1-D
model.add(Flatten(input_shape=data.train_data.shape[1:]))
# dense layers (the hidden layer)
for param in params:
model.add(Dense(param))
# ReLU activation
model.add(Activation('relu'))
# the output layer, with 10 classes
model.add(Dense(10))
# load initial weights when given
if init != None:
model.load_weights(init)
# define the loss function which is the cross entropy between prediction and true label
def fn(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels=correct,
logits=predicted /
train_temp)
# initiate the SGD optimizer with given hyper parameters
sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
# compile the Keras model, given the specified loss and optimizer
model.compile(loss=fn, optimizer=sgd, metrics=['accuracy'])
model.summary()
print("Traing a {} layer model, saving to {}".format(
len(params) + 1, file_name))
# run training with given dataset, and print progress
history = model.fit(data.train_data,
data.train_labels,
batch_size=batch_size,
validation_data=(data.validation_data,
data.validation_labels),
epochs=num_epochs,
shuffle=True)
# save model to a file
if file_name != None:
model.save(file_name)
return {'model': model, 'history': history}
def construct_inception(img_size=(32, 32), output_size):
# add a global spatial average pooling layer
b_model = self.base_model.output
b_model = GlobalAveragePooling2D()(b_model)
# let's add a fully-connected layer
b_model = Dense(1024, activation='relu')(b_model)
# and a logistic layer -- let's say we have 200 classes
self.predictions = Dense(200, activation='softmax')(b_model)
def _build_model(self):
# Neural Net for Deep-Q learning Model
model = Sequential()
model.add(Dense(24, input_dim=self.state_size, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
return model
def train(data, file_name, params, num_epochs=50, batch_size=256, train_temp=1, init=None, lr=0.01, decay=1e-5, momentum=0.9, activation="relu", optimizer_name="sgd"):
"""
Train a n-layer simple network for MNIST and CIFAR
"""
# create a Keras sequential model
model = Sequential()
# reshape the input (28*28*1) or (32*32*3) to 1-D
model.add(Flatten(input_shape=data.train_data.shape[1:]))
# dense layers (the hidden layer)
n = 0
for param in params:
n += 1
model.add(Dense(param, kernel_initializer='he_uniform'))
# ReLU activation
if activation == "arctan":
model.add(Lambda(lambda x: tf.atan(x), name=activation+"_"+str(n)))
else:
model.add(Activation(activation, name=activation+"_"+str(n)))
# the output layer, with 10 classes
model.add(Dense(10, kernel_initializer='he_uniform'))
# load initial weights when given
if init != None:
model.load_weights(init)
# define the loss function which is the cross entropy between prediction and true label
def fn(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels=correct,
logits=predicted/train_temp)
if optimizer_name == "sgd":
# initiate the SGD optimizer with given hyper parameters
optimizer = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
elif optimizer_name == "adam":
optimizer = Adam(lr=lr, beta_1 = 0.9, beta_2 = 0.999, epsilon = None, decay=decay, amsgrad=False)
# compile the Keras model, given the specified loss and optimizer
model.compile(loss=fn,
optimizer=optimizer,
metrics=['accuracy'])
model.summary()
print("Traing a {} layer model, saving to {}".format(len(params) + 1, file_name))
# run training with given dataset, and print progress
history = model.fit(data.train_data, data.train_labels,
batch_size=batch_size,
validation_data=(data.validation_data, data.validation_labels),
epochs=num_epochs,
shuffle=True)
# save model to a file
if file_name != None:
model.save(file_name)
print('model saved to ', file_name)
return {'model':model, 'history':history}
def train(data,
file_name,
params,
num_epochs=50,
batch_size=128,
train_temp=1,
init=None):
"""
Standard neural network training procedure.
"""
model = Sequential()
print(data.train_data.shape)
model.add(Conv2D(params[0], (3, 3), input_shape=data.train_data.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(params[1], (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(params[2], (3, 3)))
model.add(Activation('relu'))
model.add(Conv2D(params[3], (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(params[4]))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(params[5]))
model.add(Activation('relu'))
model.add(Dense(10))
if init != None:
model.load_weights(init)
def fn(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels=correct,
logits=predicted /
train_temp)
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss=fn, optimizer=sgd, metrics=['accuracy'])
model.fit(data.train_data,
data.train_labels,
batch_size=batch_size,
validation_data=(data.validation_data, data.validation_labels),
epochs=num_epochs,
shuffle=True)
if file_name != None:
model.save(file_name)
return model
def classifier(self, x):
x = Dropout(0.5)(x) # nn.Dropout(),
x = Dense(4096)(x) #nn.Linear(256 * 6 * 6, 4096),
x = Activation('relu')(x) #nn.ReLU(inplace=True),
x = Dropout(0.5)(x) #nn.Dropout(),
x = Dense(4096)(x) #nn.Linear(4096, 4096),
x = Activation('relu')(x) #nn.ReLU(inplace=True),
x = Dense(self.num_classes)(x) #nn.Linear(4096, num_classes),
return x
def train(data,
file_name,
params,
num_epochs=50,
batch_size=128,
train_temp=1,
init=None,
lr=0.01,
decay=1e-5,
momentum=0.9):
"""
Train a 2-layer simple network for MNIST and CIFAR
"""
# create a Keras sequential model
model = Sequential()
# reshape the input (28*28*1) or (32*32*3) to 1-D
model.add(Flatten(input_shape=data.train_data.shape[1:]))
# first dense layer (the hidden layer)
model.add(Dense(params[0]))
# \alpha = 10 in softplus, multiply input by 10
model.add(Lambda(lambda x: x * 10))
# in Keras the softplus activation cannot set \alpha
model.add(Activation('softplus'))
# so manually add \alpha to the network
model.add(Lambda(lambda x: x * 0.1))
# the output layer, with 10 classes
model.add(Dense(10))
# load initial weights when given
if init != None:
model.load_weights(init)
# define the loss function which is the cross entropy between prediction and true label
def fn(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels=correct,
logits=predicted /
train_temp)
# initiate the SGD optimizer with given hyper parameters
sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
# compile the Keras model, given the specified loss and optimizer
model.compile(loss=fn, optimizer=sgd, metrics=['accuracy'])
# run training with given dataset, and print progress
model.fit(data.train_data,
data.train_labels,
batch_size=batch_size,
validation_data=(data.validation_data, data.validation_labels),
epochs=num_epochs,
shuffle=True)
# save model to a file
if file_name != None:
model.save(file_name)
return model
def get_dae_clf():
model1 = Sequential()
model1.add(Lambda(lambda x_: x_ + 0.5, input_shape=(28, 28, 1)))
# Encoder
model1.add(Conv2D(3, (3, 3), activation="sigmoid", padding="same", activity_regularizer=regs.l2(1e-9)))
model1.add(AveragePooling2D((2, 2), padding="same"))
model1.add(Conv2D(3, (3, 3), activation="sigmoid", padding="same", activity_regularizer=regs.l2(1e-9)))
# Decoder
model1.add(Conv2D(3, (3, 3), activation="sigmoid", padding="same", activity_regularizer=regs.l2(1e-9)))
model1.add(UpSampling2D((2, 2)))
model1.add(Conv2D(3, (3, 3), activation="sigmoid", padding="same", activity_regularizer=regs.l2(1e-9)))
model1.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same', activity_regularizer=regs.l2(1e-9)))
model1.add(Lambda(lambda x_: x_ - 0.5))
model1.load_weights("./dae/mnist")
model1.compile(loss='mean_squared_error', metrics=['mean_squared_error'], optimizer='adam')
model2 = Sequential()
model2.add(Conv2D(32, (3, 3), input_shape=(28, 28, 1)))
model2.add(Activation('relu'))
model2.add(Conv2D(32, (3, 3)))
model2.add(Activation('relu'))
model2.add(MaxPooling2D(pool_size=(2, 2)))
model2.add(Conv2D(64, (3, 3)))
model2.add(Activation('relu'))
model2.add(Conv2D(64, (3, 3)))
model2.add(Activation('relu'))
model2.add(MaxPooling2D(pool_size=(2, 2)))
model2.add(Flatten())
model2.add(Dense(200))
model2.add(Activation('relu'))
model2.add(Dropout(0.5))
model2.add(Dense(200))
model2.add(Activation('relu'))
model2.add(Dense(10))
model2.load_weights("./models/mnist")
def fn(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels=correct, logits=predicted)
model2.compile(loss=fn, optimizer=SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True), metrics=['accuracy'])
model = Sequential()
model.add(model1)
model.add(model2)
model.compile(loss=fn, optimizer=SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True), metrics=['accuracy'])
return model
def build_regressor(optimizer):
regressor = Sequential()
regressor.add(Dense(units=6, kernel_initializer='uniform', activation='relu', input_dim=11))
# Improving the ANN
# Dropout Regularization to reduce overfitting if needed
regressor.add(Dropout(0.1))
regressor.add(Dense(units=6, kernel_initializer='uniform', activation='relu'))
regressor.add(Dense(units=1, kernel_initializer='uniform', activation='linear'))
regressor.compile(optimizer=optimizer, loss='mean_squared_error')
return regressor
def build_classifier(optimizer):
classifier = Sequential()
classifier.add(Dense(units=6, kernel_initializer='uniform', activation='relu', input_dim=11))
# Improving the ANN
# Dropout Regularization to reduce overfitting if needed
classifier.add(Dropout(0.1))
classifier.add(Dense(units=6, kernel_initializer='uniform', activation='relu'))
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
return classifier
def generator(data_shape):
model = Sequential()
model.add(Dense(macro._LAYER_DIM, activation='relu', input_shape=(macro._NOISE_DIM+macro._PROP_DIM,)))
#model.add(Dropout(0.2))
model.add(Dense(2*macro._LAYER_DIM, activation='relu'))
#model.add(Dropout(0.2))
#model.add(Dense(3*macro._LAYER_DIM, activation='relu'))
#model.add(Dropout(0.2))
# use sigmoid function to constrain output from 0 to 1.
model.add(Dense(data_shape, activation='sigmoid'))
return model
def discriminator(data_shape):
model = Sequential()
model.add(Dense(2*macro._LAYER_DIM, activation='relu', input_shape=(data_shape,)))
#model.add(Dropout(0.2))
#model.add(Dense(3*macro._LAYER_DIM, activation='relu', input_shape=(data_shape,)))
#model.add(Dropout(0.2))
#model.add(Dense(2*macro._LAYER_DIM, activation='relu'))
#model.add(Dropout(0.2))
model.add(Dense(macro._LAYER_DIM, activation='relu'))
#model.add(Dropout(0.2))
return model
def CreateSimpleImageModel_512():
dataIn = Input(shape=(3, ))
layer = Dense(4 * 4, activation='tanh')(dataIn)
layer = Dense(512 * 512 * 4, activation='linear')(layer)
layer = Reshape((1, 512, 512, 4))(layer)
modelOut = layer
model = Model(inputs=[dataIn], outputs=[modelOut])
adam = Adam(lr=0.005, decay=0.0001)
model.compile(loss='mean_squared_error',
optimizer=adam,
metrics=['accuracy'])
return model
def __init__(self,
restore=None,
session=None,
use_softmax=False,
use_brelu=False,
activation="relu"):
def bounded_relu(x):
return K.relu(x, max_value=1)
if use_brelu:
activation = bounded_relu
else:
activation = activation
print("inside CIFARModel: activation = {}".format(activation))
self.num_channels = 3
self.image_size = 32
self.num_labels = 10
model = Sequential()
model.add(Conv2D(64, (3, 3), input_shape=(32, 32, 3)))
model.add(Activation(activation))
model.add(Conv2D(64, (3, 3)))
model.add(Activation(activation))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3)))
model.add(Activation(activation))
model.add(Conv2D(128, (3, 3)))
model.add(Activation(activation))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation(activation))
model.add(Dense(256))
model.add(Activation(activation))
model.add(Dense(10))
if use_softmax:
model.add(Activation('softmax'))
if restore:
model.load_weights(restore)
layer_outputs = []
for layer in model.layers:
if isinstance(layer, Conv2D) or isinstance(layer, Dense):
layer_outputs.append(
K.function([model.layers[0].input], [layer.output]))
self.layer_outputs = layer_outputs
self.model = model
