def build_model():
model = Sequential()
model.add(
BinaryDense(512,
input_shape=(784, ),
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias))
model.add(Activation(binary_tanh))
model.add(
BinaryDense(512,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias))
model.add(Activation(binary_tanh))
model.add(
BinaryDense(10,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias))
opt = Adam(lr=lr_start)
model.compile(loss='squared_hinge', optimizer=opt, metrics=['accuracy'])
return model
def BinaryMobilenetV2():
model = Sequential()
model.add(BinaryConv2D(32, kernel_size=kernel_size, strides=(2, 2), H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias,input_shape=(32, 32, 3)))
model.add(BatchNormalization(axis=1))
model.add(Activation(binary_tanh))
model = InvertedResidualBlock(model, 16, (3, 3), t=1, strides=1, n=1)
model = InvertedResidualBlock(model, 24, (3, 3), t=6, strides=2, n=2)
model = InvertedResidualBlock(model, 32, (3, 3), t=6, strides=2, n=3)
model = InvertedResidualBlock(model, 64, (3, 3), t=6, strides=2, n=4)
model = InvertedResidualBlock(model, 96, (3, 3), t=6, strides=1, n=3)
model = InvertedResidualBlock(model, 160, (3, 3), t=6, strides=2, n=3)
model = InvertedResidualBlock(model, 320, (3, 3), t=6, strides=1, n=1)
model.add(BinaryConv2D(1280, H=H,kernel_size=(1,1), kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias,strides=(1, 1)))
model.add(BatchNormalization(axis=1))
model.add(Activation(binary_tanh))
model.add(Flatten())
model.add(BinaryDense(classes, H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias))
model.add(BatchNormalization(axis=1))
return model
def create_model(X_train, Y_train, X_test, Y_test):
# Hyperparameters
H = 'Glorot'
kernel_lr_multiplier = 'Glorot'
use_bias = False
epsilon = 1e-3
momentum = 0.9
epochs = 10
batch_size = {{choice([512])}}
# Number of units per layer
N = 64
# Building the model
model = Sequential()
# Input layer
model.add(BatchNormalization(input_shape=(16,), momentum=momentum, epsilon=epsilon))
model.add(Activation(binary_sigmoid))
# Hidden Layer 1
model.add(BinaryDense(N, W_regularizer=l2(0.0), H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias))
model.add(BatchNormalization())
model.add(Activation(binary_sigmoid))
# Hidden Layer 2
model.add(BinaryDense(N, W_regularizer=l2(0.0), H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias))
model.add(BatchNormalization())
model.add(Activation(binary_sigmoid))
# Hidden Layer 3
model.add(BinaryDense(N, W_regularizer=l2(0.0), H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias))
model.add(BatchNormalization())
model.add(Activation(binary_sigmoid))
# Hidden Layer 4
model.add(BinaryDense(N, W_regularizer=l2(0.0), H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias))
model.add(BatchNormalization())
model.add(Activation(binary_sigmoid))
# Output layer
model.add(BinaryDense(1, W_regularizer=l2(0.0), H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias ))
model.add(BatchNormalization())
model.add(Activation(binary_sigmoid))
# Optimiser & cost functions
opt=Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)
model.compile(loss='mse',optimizer=opt,metrics=['binary_accuracy'])
model.summary()
result = model.fit(X_train, Y_train,batch_size=batch_size,epochs= epochs,
verbose=2,
validation_data=(X_test, Y_test))
# Plotting training curve
acc = result.history['binary_accuracy']
val_acc = result.history['val_binary_accuracy']
loss = result.history['loss']
val_loss = result.history['val_loss']
epochs = range(len(acc))
plt.subplot(2, 1, 1)
plt.plot(epochs, val_acc, 'r', label='Validation acc')
plt.title('Accuracy & Loss History')
plt.ylabel('Accuracy')
plt.subplot(2, 1, 2)
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.show()
validation_acc = np.amax(result.history['val_binary_accuracy'])
tr_loss = np.amax(result.history['loss'])
print('Best validation acc of epoch:', validation_acc)
return {'loss': tr_loss, 'status': STATUS_OK, 'model': model}
def BinaryVGG16():
model = Sequential()
# 64
model.add(BinaryConv2D(64, kernel_size=kernel_size, input_shape=(32, 32, 3),
data_format='channels_last',
H=H, kernel_lr_multiplier=kernel_lr_multiplier,
padding='same', use_bias=use_bias, name='conv1'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn1'))
model.add(Activation(binary_tanh, name='act1'))
model.add(BinaryConv2D(64, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias, name='conv2'))
model.add(MaxPooling2D(pool_size=pool_size, name='pool2', data_format='channels_last'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn2'))
model.add(Activation(binary_tanh, name='act2'))
# 128
model.add(BinaryConv2D(128, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias, name='conv3'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn3'))
model.add(Activation(binary_tanh, name='act3'))
model.add(BinaryConv2D(128, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias, name='conv4'))
model.add(MaxPooling2D(pool_size=pool_size, name='pool4', data_format='channels_last'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn4'))
model.add(Activation(binary_tanh, name='act4'))
# 256
model.add(BinaryConv2D(256, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias, name='conv5'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn5'))
model.add(Activation(binary_tanh, name='act5'))
model.add(BinaryConv2D(256, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias, name='conv6'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn6'))
model.add(Activation(binary_tanh, name='act6'))
model.add(BinaryConv2D(256, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias, name='conv7'))
model.add(MaxPooling2D(pool_size=pool_size, name='pool7', data_format='channels_last'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn7'))
model.add(Activation(binary_tanh, name='act7'))
# 512
model.add(BinaryConv2D(512, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias, name='conv8'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn8'))
model.add(Activation(binary_tanh, name='act8'))
model.add(BinaryConv2D(512, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias, name='conv9'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn9'))
model.add(Activation(binary_tanh, name='act9'))
model.add(BinaryConv2D(512, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias, name='conv10'))
model.add(MaxPooling2D(pool_size=pool_size, name='pool10', data_format='channels_last'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn10'))
model.add(Activation(binary_tanh, name='act10'))
#512
model.add(BinaryConv2D(512, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias, name='conv11'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn11'))
model.add(Activation(binary_tanh, name='act11'))
model.add(BinaryConv2D(512, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias, name='conv12'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn12'))
model.add(Activation(binary_tanh, name='act12'))
model.add(BinaryConv2D(512, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_last',
padding='same', use_bias=use_bias, name='conv13'))
model.add(MaxPooling2D(pool_size=pool_size, name='pool13', data_format='channels_last'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn13'))
model.add(Activation(binary_tanh, name='act13'))
#final
model.add(Flatten())
# dense1
model.add(BinaryDense(4096, H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias, name='dense1'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn14'))
model.add(Activation(binary_tanh, name='act14'))
# dense2
model.add(BinaryDense(4096, H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias, name='dense2'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn15'))
model.add(Activation(binary_tanh, name='act15'))
#dense3
model.add(BinaryDense(classes, H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias, name='dense3'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn16'))
return model
X_test /= 255
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train,
nb_classes) * 2 - 1 # -1 or 1 for hinge loss
Y_test = np_utils.to_categorical(y_test, nb_classes) * 2 - 1
model = Sequential()
model.add(DropoutNoScale(drop_in, input_shape=(784, ), name='drop0'))
for i in range(num_hidden):
model.add(
BinaryDense(num_unit,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias,
name='dense{}'.format(i + 1)))
model.add(
BatchNormalization(epsilon=epsilon,
momentum=momentum,
name='bn{}'.format(i + 1)))
model.add(Activation(binary_tanh, name='act{}'.format(i + 1)))
model.add(DropoutNoScale(drop_hidden, name='drop{}'.format(i + 1)))
model.add(
BinaryDense(10,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias,
name='dense'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn'))
3,
H=H,
W_lr_multiplier=W_lr_multiplier,
border_mode='same',
bias=bias,
name='conv4'))
model.add(MaxPooling2D(pool_size=(2, 2), name='pool4'))
model.add(
BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn4'))
model.add(Activation(binary_tanh, name='act4'))
model.add(Flatten())
# dense1
model.add(
BinaryDense(1024,
H=H,
W_lr_multiplier=W_lr_multiplier,
bias=bias,
name='dense5'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn5'))
model.add(Activation(binary_tanh, name='act5'))
# dense2
model.add(
BinaryDense(nb_classes,
H=H,
W_lr_multiplier=W_lr_multiplier,
bias=bias,
name='dense6'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn6'))
opt = Adam(lr=lr_start)
model.compile(loss='squared_hinge', optimizer=opt, metrics=['acc'])
# conv3
model.add(BinaryConv2D(256, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_first',
padding='same', use_bias=use_bias, name='conv3'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn3'))
model.add(Activation(binary_tanh, name='act3'))
# conv4
model.add(BinaryConv2D(256, kernel_size=kernel_size, H=H, kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_first',
padding='same', use_bias=use_bias, name='conv4'))
model.add(MaxPooling2D(pool_size=pool_size, name='pool4'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn4'))
model.add(Activation(binary_tanh, name='act4'))
model.add(Flatten())
# dense1
model.add(BinaryDense(1024, H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias, name='dense5'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn5'))
model.add(Activation(binary_tanh, name='act5'))
# dense2
model.add(BinaryDense(classes, H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias, name='dense6'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn6'))
opt = Adam(lr=lr_start)
model.compile(loss='squared_hinge', optimizer=opt, metrics=['acc'])
model.summary()
lr_scheduler = LearningRateScheduler(lambda e: lr_start * lr_decay ** e)
history = model.fit(X_train, Y_train,
batch_size=batch_size, epochs=epochs,
verbose=1, validation_data=(X_test, Y_test),
callbacks=[lr_scheduler])
lr_start = 1e-3
lr_end = 1e-4
lr_decay = (lr_end / lr_start)**(1. / epochs)
# BN
epsilon = 1e-6
momentum = 0.9
# dropout
drop_in = 0.2
drop_hidden = 0.5
# Step 2: Build the Model
model = Sequential()
model.add(BinaryDense(512, input_shape=(784,), H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias))
model.add(Activation(binary_tanh))
model.add(BinaryDense(512, H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias))
model.add(Activation(binary_tanh))
model.add(BinaryDense(10, H=H, kernel_lr_multiplier=kernel_lr_multiplier, use_bias=use_bias))
# Printing the model
print(model.summary())
for layer in model.layers:
h = layer.get_weights()
print(h)
opt = Adam(lr=lr_start)
# Step 3: Compile the Model
def build_model(kernel_size, channels, img_rows, img_cols, H,
kernel_lr_multiplier, use_bias, epsilon, momentum):
model = Sequential()
# conv1
model.add(
BinaryConv2D(128,
kernel_size=kernel_size,
input_shape=(channels, img_rows, img_cols),
data_format='channels_first',
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
padding='same',
use_bias=use_bias,
name='conv1'))
model.add(
MaxPooling2D(pool_size=pool_size,
name='pool1',
data_format='channels_first'))
model.add(
BatchNormalization(epsilon=epsilon,
momentum=momentum,
axis=1,
name='bn1'))
model.add(Activation(binary_tanh, name='act1'))
# conv2
model.add(
BinaryConv2D(128,
kernel_size=kernel_size,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_first',
padding='same',
use_bias=use_bias,
name='conv2'))
model.add(
MaxPooling2D(pool_size=pool_size,
name='pool2',
data_format='channels_first'))
model.add(
BatchNormalization(epsilon=epsilon,
momentum=momentum,
axis=1,
name='bn2'))
model.add(Activation(binary_tanh, name='act2'))
# conv3
model.add(
BinaryConv2D(128,
kernel_size=kernel_size,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_first',
padding='same',
use_bias=use_bias,
name='conv3'))
model.add(
MaxPooling2D(pool_size=pool_size,
name='pool3',
data_format='channels_first'))
model.add(
BatchNormalization(epsilon=epsilon,
momentum=momentum,
axis=1,
name='bn3'))
model.add(Activation(binary_tanh, name='act3'))
# conv4
model.add(
BinaryConv2D(256,
kernel_size=kernel_size,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
data_format='channels_first',
padding='same',
use_bias=use_bias,
name='conv4'))
model.add(
MaxPooling2D(pool_size=pool_size,
name='pool4',
data_format='channels_first'))
model.add(
BatchNormalization(epsilon=epsilon,
momentum=momentum,
axis=1,
name='bn4'))
model.add(Activation(binary_tanh, name='act4'))
model.add(Flatten())
# dense1
model.add(
BinaryDense(256,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias,
name='dense5'))
model.add(
BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn5'))
model.add(Activation(binary_tanh, name='act5'))
# dense2
model.add(
BinaryDense(classes,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias,
name='dense6'))
model.add(
BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn6'))
opt = Adam(lr=lr_start)
model.compile(loss='squared_hinge', optimizer=opt, metrics=['acc'])
model.summary()
return model
X_test /= 255
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train,
nb_classes) * 2 - 1 # -1 or 1 for hinge loss
Y_test = np_utils.to_categorical(y_test, nb_classes) * 2 - 1
model = Sequential()
model.add(DropoutNoScale(drop_in, input_shape=(784, ), name='drop0'))
for i in range(num_hidden):
model.add(
BinaryDense(num_unit,
H=H,
W_lr_multiplier=W_lr_multiplier,
bias=bias,
name='dense{}'.format(i + 1)))
model.add(
BatchNormalization(epsilon=epsilon,
momentum=momentum,
name='bn{}'.format(i + 1)))
model.add(Activation(binary_tanh, name='act{}'.format(i + 1)))
model.add(DropoutNoScale(drop_hidden, name='drop{}'.format(i + 1)))
model.add(
BinaryDense(10,
H=H,
W_lr_multiplier=W_lr_multiplier,
bias=bias,
name='dense'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn'))
padding='same',
use_bias=use_bias,
name='conv4'))
model.add(
MaxPooling2D(pool_size=pool_size,
name='pool4',
data_format='channels_first'))
model.add(
BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn4'))
model.add(Activation(binary_tanh, name='act4'))
model.add(Flatten())
# dense1
model.add(
BinaryDense(128,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias,
name='dense5'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn5'))
model.add(Activation(binary_tanh, name='act5'))
# dense2
model.add(
BinaryDense(classes,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias,
name='dense6'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn6'))
opt = Adam(lr=lr_start)
model.compile(loss='squared_hinge', optimizer=opt, metrics=['acc'])
model.add(
MaxPooling2D(pool_size=pool_size,
name='pool6',
data_format='channels_first'))
model.add(
BatchNormalization(epsilon=epsilon, momentum=momentum, axis=1, name='bn6'))
model.add(Activation(binary_tanh, name='act6'))
# In[10]:
model.add(Flatten())
# dense1
model.add(
BinaryDense(1024,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias,
name='dense7'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn7'))
model.add(Activation(binary_tanh, name='act7'))
# dense2
model.add(
BinaryDense(1024,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias,
name='dense8'))
model.add(BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn8'))
model.add(Activation(binary_tanh, name='act8'))
# dense3
model.add(
def Binary_Net(kernel_size, img_rows, img_cols, channels, data_format, H,
kernel_lr_multiplier, use_bias, epsilon, momentum, classes,
pool_size):
model = Sequential()
# conv1
model.add(
BinaryConv2D(128,
kernel_size=kernel_size,
input_shape=(img_rows, img_cols, channels),
data_format=data_format,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
padding='same',
use_bias=use_bias,
name='conv1'))
model.add(
BatchNormalization(epsilon=epsilon,
momentum=momentum,
axis=-1,
name='bn1'))
model.add(Activation(binary_tanh, name='act1'))
# conv2
model.add(
BinaryConv2D(128,
kernel_size=kernel_size,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
data_format=data_format,
padding='same',
use_bias=use_bias,
name='conv2'))
model.add(
MaxPooling2D(pool_size=pool_size,
name='pool2',
data_format=data_format))
model.add(
BatchNormalization(epsilon=epsilon,
momentum=momentum,
axis=-1,
name='bn2'))
model.add(Activation(binary_tanh, name='act2'))
# conv3
model.add(
BinaryConv2D(256,
kernel_size=kernel_size,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
data_format=data_format,
padding='same',
use_bias=use_bias,
name='conv3'))
model.add(
BatchNormalization(epsilon=epsilon,
momentum=momentum,
axis=-1,
name='bn3'))
model.add(Activation(binary_tanh, name='act3'))
# conv4
model.add(
BinaryConv2D(256,
kernel_size=kernel_size,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
data_format=data_format,
padding='same',
use_bias=use_bias,
name='conv4'))
model.add(
MaxPooling2D(pool_size=pool_size,
name='pool4',
data_format=data_format))
model.add(
BatchNormalization(epsilon=epsilon,
momentum=momentum,
axis=-1,
name='bn4'))
model.add(Activation(binary_tanh, name='act4'))
model.add(Flatten())
# dense1
model.add(
BinaryDense(1024,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias,
name='dense5'))
model.add(
BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn5'))
model.add(Activation(binary_tanh, name='act5'))
# dense2
model.add(
BinaryDense(classes,
H=H,
kernel_lr_multiplier=kernel_lr_multiplier,
use_bias=use_bias,
name='dense6'))
model.add(
BatchNormalization(epsilon=epsilon, momentum=momentum, name='bn6'))
return model
