def build_network(self):
states = layers.Input(shape=self.state_size, name='states')
layer_1 = layers.Dense(units=self.num_nodes,
activation='relu',
kernel_regularizer=regularizers.l2(
self.reg))(states)
layer_1 = layers.GaussianNoise(.1)(layer_1)
layer_2 = layers.Dense(units=self.num_nodes,
activation='relu',
kernel_regularizer=regularizers.l2(
self.reg))(layer_1)
layer_2 = layers.GaussianNoise(.1)(layer_2)
layer_3 = layers.Dense(units=self.num_nodes,
activation='relu',
kernel_regularizer=regularizers.l2(
self.reg))(layer_2)
layer_3 = layers.GaussianNoise(.1)(layer_3)
output = layers.Dense(
self.action_size[0],
activation='tanh',
kernel_initializer=keras.initializers.RandomUniform(
minval=-5e-2, maxval=5e-2))(layer_3)
output = layers.Lambda(lambda i: i * self.action_range)(output)
return models.Model(inputs=[states], outputs=[output]), output
def set_VGG16_model():
# 设置卷积网络基础VGG16
conv_base = VGG16(include_top=False, weights='imagenet', input_shape=(224, 224, 3))
conv_base.trainable = True
# 组建网络
model = models.Sequential()
model.add(layers.GaussianNoise(0.125, input_shape=(224, 224, 3)))
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.GaussianNoise(0.25))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1024, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
# 解冻VGG16卷积的最后几层
set_trainable = False
for layer in conv_base.layers:
if layer.name == 'block4_conv1':
set_trainable = True
if set_trainable:
layer.trainable = True
else:
layer.trainable = False
return model
# model.load_weights(r'')
def build_generator(self):
# Generates samples from noisey class label conditions
noise = kls.Input(shape=(self.latent_dim, ))
label = kls.Input(shape=(self.num_classes + 1, ))
model_input = kls.concatenate([noise, label])
x = kls.Dense(256, )(
model_input) #activity_regularizer=regularizers.l1(0.0001)
x = kls.GaussianNoise(.1)(x)
x = kls.Activation('relu')(x)
x = kls.BatchNormalization(momentum=0.1)(x)
x = kls.Dense(256, )(x)
x = kls.GaussianNoise(.1)(x)
x = kls.Activation('relu')(x)
x = kls.BatchNormalization(momentum=0.1)(x)
# x = kls.Dropout(0.3)(x)
# x = kls.Dense(256)(x)
# x = kls.GaussianNoise(.4)(x)
# x = kls.Activation('relu')(x)
# x = kls.BatchNormalization(momentum=0.5)(x)
x = kls.Dense(self.gesture_size, )(x)
x = kls.Activation('linear')(x)
# x = kls.ActivityRegularization(0.0,0.01)(x)
return Model([noise, label], x)
def run(self):
(train_images, train_target), (test_images, test_target) = mnist.load_data()
train_target = np_utils.to_categorical(train_target)
test_target = np_utils.to_categorical(test_target)
train_images = (train_images).astype('float32')
test_images = (test_images).astype('float32')
train_images = train_images.reshape(60000, 28 * 28)
test_images = test_images.reshape(10000, 28 * 28)
scaler = StandardScaler()
scaler.fit(train_images)
train_images_sc = scaler.transform(train_images)
test_images_sc = scaler.transform(test_images)
pca = PCA(n_components=500)
pca.fit(train_images)
NCOMPONENTS = 100
pca = PCA(n_components=NCOMPONENTS)
train_images_pca = pca.fit_transform(train_images_sc)
test_images_pca = pca.transform(test_images_sc)
pca_std = np.std(train_images_pca)
model = models.Sequential()
layer = 1
units = 128
model.add(layers.Dense(units, input_dim=NCOMPONENTS, activation='relu'))
model.add(layers.GaussianNoise(pca_std))
for i in range(layer):
model.add(layers.Dense(units, activation='relu'))
model.add(layers.GaussianNoise(pca_std))
model.add(layers.Dropout(0.1))
model.add(layers.Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['categorical_accuracy'])
model.fit(train_images_pca, train_target, epochs=100, batch_size=256, validation_split=0.15, verbose=2)
predictions = model.predict_classes(test_images_pca, verbose=0)
y_true = np.argmax(test_target, axis=1)
cm = confusion_matrix(y_true, predictions)
print('cm: ', cm)
ax = plt.subplot()
sns.heatmap(cm, annot=True, ax=ax, square=True, fmt='g')
ax.set_xlabel('Predicted')
ax.set_ylabel('True')
ax.set_title('Confusion Matrix')
ax.xaxis.set_ticklabels(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
ax.yaxis.set_ticklabels(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
plt.show()
def build_model(self):
"""Build an actor (policy) network that maps states -> actions."""
# Define input layer (states)
states = layers.Input(shape=(self.state_size, ), name='states')
# Add hidden layers
net = layers.Dense(units=32)(states)
net = layers.BatchNormalization()(net)
net = layers.Activation('relu')(net)
# Based on: https://openai.com/blog/better-exploration-with-parameter-noise/
net = layers.GaussianNoise(1.0)(net)
net = layers.Dense(units=64)(net)
net = layers.BatchNormalization()(net)
net = layers.Activation('relu')(net)
net = layers.GaussianNoise(1.0)(net)
net = layers.Dense(units=32)(net)
net = layers.BatchNormalization()(net)
net = layers.Activation('relu')(net)
net = layers.GaussianNoise(1.0)(net)
# Try different layer sizes, activations, add batch normalization, regularizers, etc.
# Add final output layer with sigmoid activation
raw_actions = layers.Dense(units=self.action_size,
activation='sigmoid',
name='raw_actions')(net)
# Scale [0, 1] output for each action dimension to proper range
actions = layers.Lambda(lambda x:
(x * self.action_range) + self.action_low,
name='actions')(raw_actions)
# Create Keras model
self.model = models.Model(inputs=states, outputs=actions)
# Define loss function using action value (Q value) gradients
action_gradients = layers.Input(shape=(self.action_size, ))
loss = K.mean(-action_gradients * actions)
# Incorporate any additional losses here (e.g. from regularizers)
# Define optimizer and training function
optimizer = optimizers.Adam(lr=self.lr)
# optimizer = optimizers.RMSprop(lr=self.lr)
# optimizer = optimizers.SGD(lr=self.lr)
updates_op = optimizer.get_updates(params=self.model.trainable_weights,
loss=loss)
self.train_fn = K.function(
inputs=[self.model.input, action_gradients,
K.learning_phase()],
outputs=[],
updates=updates_op)
def mk_conv_32(*, channels):
i = kr.Input((32, 32, 4), name='x0')
cc_stack = [
# kr.BatchNormalization(),
kr.GaussianNoise(0.025),
kr.Conv2D(10, 1, name='cconv_0'),
kr.LeakyReLU(alpha=0.4),
kr.Conv2D(channels, 1, name='cconv_1'),
kr.LeakyReLU(alpha=0.4),
]
h = apply_layers(i, cc_stack)
conv_stack_0 = [
kr.GaussianNoise(0.025),
kr.Conv2D(64, 3, activation='relu'),
kr.Conv2D(128, 3, activation='relu'),
kr.Conv2D(256, 3, activation='relu'),
kr.Conv2D(256, 3, strides=2, activation='relu'),
]
h = apply_layers(h, conv_stack_0)
ga0 = kr.GlobalAveragePooling2D()(h)
gm0 = kr.GlobalMaxPooling2D()(h)
conv_stack_1 = [
kr.Conv2D(196, 3, activation='relu'),
kr.Conv2D(196, 3, strides=2, activation='relu'),
kr.Conv2D(196, 3, activation='relu'),
kr.Flatten(),
]
h = apply_layers(h, conv_stack_1)
cat = kr.Concatenate()([h, gm0, ga0])
head = [
kr.Dropout(0.5),
kr.Dense(512, activation='elu'),
kr.Dropout(0.5),
kr.Dense(256, activation='elu'),
kr.Dropout(0.5),
kr.Dense(Y_TRAIN.shape[1], activation='sigmoid', name='labs'),
]
y = apply_layers(cat, head)
m = krm.Model(inputs=[i], outputs=[y], name='conv_32')
m.compile(loss=f2_crossentropy,
optimizer='adam',
metrics=['binary_accuracy'])
return m
def build_model(self):
"""Build actor(policy) network that maps states -> actions."""
#Define input layer (states)
states = layers.Input(shape=(self.state_size,), name='states')
#Add hidden layers, try different num layers, sizes, activation, batch_normalization, regularizers, etc
net = layers.Dense(units=128, )(states)
#net = layers.BatchNormalization()(net)
net = layers.Activation('relu')(net)
net = layers.GaussianNoise(1.0)(net)
net = layers.Dense(units=256)(net)
#net = layers.BatchNormalization()(net)
net = layers.Activation('relu')(net)
net = layers.GaussianNoise(1.0)(net)
net = layers.Dense(units=128)(net)
#net = layers.BatchNormalization()(net)
net = layers.Activation('relu')(net)
net = layers.GaussianNoise(1.0)(net)
#final layer with tanh which is a scaled sigmoid activation (between -1 and 1)
#https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0
#raw_actions = layers.Dense(units=self.action_size, activation='sigmoid', name='raw_actions')(net)
raw_actions = layers.Dense(units=self.action_size, activation='tanh', name='raw_actions')(net)
#rescale to -2 and +2 with Lambda
raw_actions = layers.Lambda(lambda x: x * 2.)(raw_actions)
#Create keras model
self.model = models.Model(input=states, outputs=(raw_actions))
self.model.summary()
"""these lines are called from DDPG once the gradients of Q-value
obtained from critic
we define action_gradients which appears in K.function so it chains back from
K.function back up here I think
Define loss function using action value (Q value) gradients from critic
placeholder below"""
#scaled_actions = raw_actions * 2
action_gradients = layers.Input(shape=(self.action_size,)) #returns tensor
#rescale actions here to calculate loss (raw_actions *2)???????????????????????????
loss = K.mean(-action_gradients * raw_actions)
#####################################################
#other losses here, ie. from regularizers
#TODO: add entropy??? but that is OU noise
#Define optimizer and training function for actor_local
optimizer = optimizers.Adam(lr=self.actor_local_lr)
updates_op = optimizer.get_updates(params=self.model.trainable_weights, loss=loss)
"""self.train_fn can be called in learn function
call:
self.actor_local.train_fn([states, action_gradients, 1])
"""
self.train_fn = K.function(inputs=[self.model.input, action_gradients, K.learning_phase()], \
outputs=[], updates=updates_op)
def network(self):
states = layers.Input(shape=(self.state_dim, ))
#
x = layers.Dense(32,
activation='tanh',
kernel_initializer=keras.initializers.RandomNormal(
mean=0.0, stddev=0.1, seed=None),
bias_initializer='zeros')(states)
x = layers.BatchNormalization()(x)
x = layers.GaussianNoise(self.gaussian_std)(x)
#
# x = layers.Dense(64, activation='relu',
# kernel_initializer=keras.initializers.RandomNormal(mean=0.0, stddev=0.1, seed=None),
# bias_initializer='zeros')(x)
# x = layers.GaussianNoise(self.gaussian_std)(x)
#
x = layers.Dense(32,
activation='tanh',
kernel_initializer=keras.initializers.RandomNormal(
mean=0.0, stddev=0.1, seed=None),
bias_initializer='zeros')(x)
x = layers.BatchNormalization()(x)
x = layers.GaussianNoise(self.gaussian_std)(x)
#
actions = layers.Dense(
self.act_dim,
activation='linear',
kernel_initializer=keras.initializers.RandomNormal(mean=0.0,
stddev=0.1,
seed=None),
bias_initializer='zeros')(x)
actions = layers.GaussianNoise(0.5)(actions)
# actions = layers.Lambda(lambda x: K.clip(x, -self.clip, self.clip), (1, ))(actions)
#
self.model = models.Model(inputs=states, outputs=actions)
# Define loss function using action value (Q value) gradients
action_gradients = layers.Input(shape=(self.act_dim, ))
loss = 0.000001 * K.mean(-action_gradients * actions)
# Define optimizer and training function
optimizer = optimizers.Adam()
updates_op = optimizer.get_updates(params=self.model.trainable_weights,
loss=loss)
self.train_fn = K.function(
inputs=[self.model.input, action_gradients,
K.learning_phase()],
outputs=[loss],
updates=updates_op)
def set_ResNet_model():
conv_base = ResNet50(include_top=False,
weights='imagenet',
input_shape=(224, 224, 3))
conv_base.trainable = True
# 组建网络
model = models.Sequential()
model.add(layers.GaussianNoise(0.125, input_shape=(224, 224, 3)))
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
# 解冻ResNet50的最后几层
set_trainable = False
for layer in conv_base.layers:
if layer.name == 'conv5_block1_1_conv':
set_trainable = True
if set_trainable:
layer.trainable = True
else:
layer.trainable = False
return model
def setVGG16(dropout_rate, lr):
main_input = layers.Input([config.img_size, config.img_size, 1])
x = layers.BatchNormalization()(main_input)
x = layers.GaussianNoise(0.01)(x)
base_model = VGG16(weights=None, input_tensor=x, include_top=False)
# flatten = layers.GlobalAveragePooling2D()(base_model.output)
flatten = Flatten()(base_model.output)
fc = Dense(
2048,
activation='relu',
kernel_regularizer=l2(0.001),
bias_regularizer=l2(0.001),
)(flatten)
fc = Dropout(dropout_rate)(fc)
fc = Dense(
2048,
activation='relu',
kernel_regularizer=l2(0.001),
bias_regularizer=l2(0.001),
)(fc)
fc = Dropout(dropout_rate)(fc)
predictions = Dense(config.class_num, activation="softmax")(fc)
model = keras.Model(inputs=main_input, outputs=predictions, name='vgg16')
optimizer = keras.optimizers.Adam(lr)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['categorical_accuracy'])
return model
def convo_block(y, filtros, num_conv, res=0):
if (res):
x = y
s = 2
for i in range(num_conv):
y = layers.Conv2D(filtros,
kernel_size=(3, 3),
strides=(s, s),
padding='same')(y)
s = 1
y = layers.BatchNormalization()(y)
y = layers.GaussianNoise(0.3)(y)
if (i < num_conv - 1):
y = layers.ReLU()(y)
if (res):
x = layers.Conv2D(filtros,
kernel_size=(1, 1),
strides=(2, 2),
padding='same')(x)
y = layers.add([x, y])
y = layers.ReLU()(y)
else:
y = layers.MaxPooling2D(pool_size=(2, 2))(y)
return y
def vgg_like_branch(input_tensor, small_mode=True):
filters = [16, 32, 64, 128] if small_mode else [64, 128, 256, 512, 640]
conv0 = L.Conv2D(filters[3], (3, 3), padding='same')(input_tensor)
conv0 = L.BatchNormalization(axis=-1)(conv0) # 9-2
conv0 = L.advanced_activations.LeakyReLU(alpha=0.2)(conv0)
conv1 = L.Conv2D(filters[2], (1, 1), padding='same')(conv0)
conv1 = L.advanced_activations.LeakyReLU(alpha=0.2)(conv1)
conv2 = L.Conv2D(filters[1], (3, 3), padding='same')(conv1)
conv2 = L.advanced_activations.LeakyReLU(alpha=0.2)(conv2)
conv2 = L.GaussianNoise(stddev=0.2)(conv2) # 7-2
# conv3 = L.Conv2D(filters[3], (3, 3), padding='same')(conv2)
conv3 = L.MaxPool2D(pool_size=(2, 2), padding='same')(conv2)
conv3 = L.Conv2D(filters[2], (3, 3), padding='same')(conv3) # 5-2
conv3 = L.BatchNormalization(axis=-1)(conv3)
conv3 = L.advanced_activations.LeakyReLU(alpha=0.2)(conv3)
conv4 = L.Conv2D(filters[3], (3, 3), padding='same')(conv3) # 3-2
conv4 = L.advanced_activations.LeakyReLU(alpha=0.2)(conv4)
conv4 = L.Flatten()(conv4)
conv4 = L.Dense(2048)(conv4)
conv4 = L.advanced_activations.LeakyReLU(alpha=0.2)(conv4)
return conv4
def generate_model(sizeH, sizeW, num_classes, num_conv_blocks, fully_depth):
shape = (sizeH, sizeW, 3)
filtros = 32
n_conv = 1
#Convolutional part
for i in range(num_conv_blocks):
if (i == 0):
input_layer = layers.Input(shape=shape)
x = convo_block(input_layer, filtros, n_conv)
else:
x = convo_block(x, filtros, n_conv, res=1)
n_conv = n_conv * 2
if filtros < 256:
filtros = filtros * 2
x = layers.Flatten()(x)
#Fully connected part
for i in range(fully_depth):
x = layers.Dense(512)(x)
x = layers.BatchNormalization()(x)
x = layers.GaussianNoise(0.3)(x)
x = layers.ReLU()(x)
x = layers.Dense(num_classes, activation='softmax')(x)
model = models.Model(inputs=[input_layer], outputs=[x])
return model
def build_model(shape, vocab_size):
"""构建模型
"""
input_layer = layers.Input(shape=shape)
m = input_layer
m = layers.Embedding(vocab_size, 64)(m)
m = layers.Dropout(0.1)(m)
m = layers.GRU(
32,
return_sequences=True,
# recurrent_dropout=0.2,
kernel_regularizer=regularizers.l2(0.001))(m)
m = layers.GRU(
32,
return_sequences=True,
# recurrent_dropout=0.2,
kernel_regularizer=regularizers.l2(0.001))(m)
atten = m
atten = layers.Flatten()(atten)
atten = layers.Dense(shape[0], activation='softmax')(atten)
atten = layers.RepeatVector(32)(atten)
atten = layers.Permute((2, 1))(atten)
m = layers.Multiply()([m, atten])
# m = layers.Add()([m, emb])
m = layers.Flatten()(m)
m = layers.GaussianNoise(0.01)(m)
m = layers.Dense(300,
activation='linear',
kernel_regularizer=regularizers.l2(0.01))(m)
m = layers.BatchNormalization()(m)
m = layers.Activation('tanh')(m)
m = layers.Dropout(0.4)(m)
m = layers.Dense(300,
activation='linear',
kernel_regularizer=regularizers.l2(0.01))(m)
m = layers.BatchNormalization()(m)
m = layers.Activation('tanh')(m)
m = layers.Dropout(0.4)(m)
m = layers.Dense(len(LABEL_DICT), activation='softmax')(m)
atten_model = models.Model(inputs=[input_layer], outputs=atten)
model = models.Model(inputs=[input_layer], outputs=m)
optimizer = optimizers.Adam(lr=0.001, clipnorm=5.)
model.compile(optimizer, 'categorical_crossentropy', metrics=['accuracy'])
model.summary()
return model, atten_model
def create_complex_model(param: Param) -> keras.Model:
inputs = keras.Input((28, 28, 1))
x = inputs
if param is not None:
x = layers.GaussianNoise(param.noise_stddev)(x)
x_re = x
x_im = layers.Lambda(lambda z: K.zeros((1, 28, 28, 1)))([])
for i in range(len(param.conv_filters)):
x_re, x_im = complexize_kernel(layers.Conv2D,
param.conv_filters[i],
kernel_size=param.kernel_sizes[i],
strides=param.strides[i],
padding='same',
activation=layers.Activation("tanh"))(
x_re, x_im)
x_re = layers.BatchNormalization(axis=-1)(x_re)
x_im = layers.BatchNormalization(axis=-1)(x_im)
if param.pool_sizes[i] is not None:
pool_size = param.pool_sizes[i]
pool_strides = param.pool_strides[i]
x_re = layers.AveragePooling2D(pool_size=pool_size,
strides=pool_strides)(x_re)
x_im = layers.AveragePooling2D(pool_size=pool_size,
strides=pool_strides)(x_im)
if param.conv_dropout_rates[i] is not None:
dropout_rate = param.conv_dropout_rates[i]
x_re = layers.Dropout(dropout_rate)(x_re)
x_im = layers.Dropout(dropout_rate)(x_im)
x_re = layers.Flatten()(x_re)
x_im = layers.Flatten()(x_im)
for units, dropout_rate in zip(param.dense_units,
param.dense_dropout_rates):
x_re, x_im = complexize_kernel(layers.Dense,
units,
activation=layers.Activation("tanh"))(
x_re, x_im)
if dropout_rate is not None:
x_re = layers.Dropout(dropout_rate)(x_re)
x_im = layers.Dropout(dropout_rate)(x_im)
# x = layers.Lambda(lambda d: K.sqrt(K.square(d[0]) + K.square(d[1])))([x_re, x_im])
if param.l2_constrained_scale:
x = layers.Lambda(lambda z: K.l2_normalize(z, axis=1) * param.
l2_constrained_scale)(x_re)
outputs = layers.Dense(10,
kernel_constraint=keras.constraints.UnitNorm(),
use_bias=False)(x)
else:
outputs = layers.Dense(10)(x_re)
model = keras.Model(inputs=inputs, outputs=outputs)
if param.center_loss_margin:
loss = CenterLoss(param.center_loss_margin)
else:
loss = tf.losses.softmax_cross_entropy
model.compile(loss=loss, optimizer='adam', metrics=['accuracy'])
return model
def unet():
input_img = layers.Input((256, 256, 3), name='RGB_Input')
pp_in_layer = input_img
if NET_SCALING is not None:
pp_in_layer = layers.AvgPool2D(NET_SCALING)(pp_in_layer)
pp_in_layer = layers.GaussianNoise(GAUSSIAN_NOISE)(pp_in_layer)
pp_in_layer = layers.BatchNormalization()(pp_in_layer)
c1 = layers.Conv2D(8, (3, 3), activation='relu',
padding='same')(pp_in_layer)
c1 = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(c1)
p1 = layers.MaxPooling2D((2, 2))(c1)
c2 = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(p1)
c2 = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(c2)
p2 = layers.MaxPooling2D((2, 2))(c2)
c3 = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(p2)
c3 = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(c3)
p3 = layers.MaxPooling2D((2, 2))(c3)
c4 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(p3)
c4 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(c4)
p4 = layers.MaxPooling2D(pool_size=(2, 2))(c4)
c5 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(p4)
c5 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(c5)
u6 = upsample(64, (2, 2), strides=(2, 2), padding='same')(c5)
u6 = layers.concatenate([u6, c4])
c6 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(u6)
c6 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(c6)
u7 = upsample(32, (2, 2), strides=(2, 2), padding='same')(c6)
u7 = layers.concatenate([u7, c3])
c7 = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(u7)
c7 = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(c7)
u8 = upsample(16, (2, 2), strides=(2, 2), padding='same')(c7)
u8 = layers.concatenate([u8, c2])
c8 = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(u8)
c8 = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(c8)
u9 = upsample(8, (2, 2), strides=(2, 2), padding='same')(c8)
u9 = layers.concatenate([u9, c1], axis=3)
c9 = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(u9)
c9 = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(c9)
d = layers.Conv2D(1, (1, 1), activation='sigmoid')(c9)
d = layers.Cropping2D((EDGE_CROP, EDGE_CROP))(d)
d = layers.ZeroPadding2D((EDGE_CROP, EDGE_CROP))(d)
if NET_SCALING is not None:
d = layers.UpSampling2D(NET_SCALING)(d)
seg_model = models.Model(input[input_img], outputs=[d])
return seg_model
def res_dense_block_explosive(inputs, dim, activation='linear'):
a = attention_2d_block(inputs)
a = layers.BatchNormalization()(a)
a = layers.GaussianNoise(0.3)(a)
a = layers.Concatenate()([inputs, a])
a = layers.Dense(dim, activation=activation)(a)
a = layers.BatchNormalization()(a)
a = layers.Concatenate()([inputs, a])
a = layers.BatchNormalization()(a)
return a
def build_discriminator(self):
inputs = kls.Input(shape=(self.gesture_size, ))
x = kls.GaussianNoise(.4)(inputs)
x = kls.Dense(300)(x)
x = kls.Activation('relu')(x)
x = kls.GaussianNoise(.4)(x)
x = kls.Dense(300)(x)
x = kls.Activation('relu')(x)
x = kls.Dropout(0.3)(x)
# Determine validity and label of the image
validity = kls.Dense(1, activation="sigmoid",
name='d_output_source')(x)
label = kls.Dense(self.num_classes + 1,
activation="softmax",
name='d_output_class')(x)
return Model(inputs, [validity, label])
def bottleneck_Block(input,
out_filters,
strides=(1, 1),
with_conv_shortcut=False):
expansion = 4
de_filters = int(out_filters / expansion)
x = Conv2D(de_filters, 1, use_bias=False,
kernel_initializer='he_normal')(input)
x = BatchNormalization(axis=3)(x)
x = kl.GaussianNoise(GNOISE)(x)
x = Activation('relu')(x)
x = Conv2D(de_filters,
3,
strides=strides,
padding='same',
use_bias=False,
kernel_initializer='he_normal')(x)
x = BatchNormalization(axis=3)(x)
x = kl.GaussianNoise(GNOISE)(x)
x = Activation('relu')(x)
x = Conv2D(out_filters, 1, use_bias=False,
kernel_initializer='he_normal')(x)
x = BatchNormalization(axis=3)(x)
x = kl.GaussianNoise(GNOISE)(x)
if with_conv_shortcut:
residual = Conv2D(out_filters,
1,
strides=strides,
use_bias=False,
kernel_initializer='he_normal')(input)
residual = BatchNormalization(axis=3)(residual)
x = add([x, residual])
else:
x = add([x, input])
x = Activation('relu')(x)
return x
def build_discriminator():
inputs = kls.Input(shape=(16, ))
x = kls.GaussianNoise(.4)(inputs)
x = kls.Dense(300)(x)
x = kls.Activation('relu')(x)
x = kls.GaussianNoise(.4)(x)
x = kls.Dense(300)(x)
x = kls.Activation('relu')(x)
x = kls.Dropout(0.3)(x)
label = kls.Dense(8, activation="softmax", name='d_output_class')(x)
discriminator = Model(inputs, label)
discriminator.compile(
loss='categorical_crossentropy',
optimizer=Adam(0.0002, .5, decay=1e-7), #SGD(0.1, 0.1), #
metrics=['accuracy'])
return discriminator
def mk_conv_16(channels, loss_weights=None, mode='joint'):
i = kr.Input((16, 16, channels), name='x0')
conv_stack = [
kr.GaussianNoise(0.05),
kr.Conv2D(32, 3, strides=1, activation='relu', name='conv16_c0'),
kr.Conv2D(128, 3, strides=1, activation='relu', name='conv16_c1'),
kr.Conv2D(196, 3, strides=1, activation='relu', name='conv16_c2'),
kr.Conv2D(256, 3, strides=1, activation='relu', name='conv16_c3'),
kr.Conv2D(196, 3, strides=1, activation='relu', name='conv16_c4'),
kr.Conv2D(196, 3, strides=1, activation='relu', name='conv16_c5'),
kr.Conv2D(196, 3, strides=1, activation='relu', name='conv16_c6'),
kr.Conv2D(128, 2, strides=1, activation='relu', name='conv16_c7'),
]
h = apply_layers(i, conv_stack)
head = [
kr.Flatten(),
kr.Dense(128, activation='elu', name='conv16_d0'),
kr.Dropout(0.5),
kr.Dense(128, activation='elu', name='conv16_d1'),
]
h = apply_layers(h, head)
if mode == 'joint':
ys = [kr.Dense(NL, activation='sigmoid', name='labs')(h)]
loss = f2_crossentropy
metrics = ['binary_accuracy']
elif mode in {'binary', 'indiv'}:
loss = f2_crossentropy
metrics = ['categorical_accuracy']
if mode == 'binary':
ys = [
kr.Dense(2, activation='softmax', name=f'lab_{lab}')(h)
for lab in LABELS
]
else:
ys = [kr.Dense(2, activation='softmax', name='lab')(h)]
else:
raise ValueError(f'invalid mode {mode}')
m = krm.Model(inputs=[i], outputs=ys, name=f'conv_16_{mode}')
m.compile(loss=loss, metrics=metrics, optimizer='adam')
return m
def make_dsae(image_size_x=None, image_size_y=None, n_channels=3):
original_dim = image_size_x * image_size_y
output_dim = image_size_x * image_size_y * n_channels
# network parameters 640x360 image
input_shape = (image_size_x * image_size_y * n_channels, )
intermediate_dim = 250
latent_dim = 5
# VAE model = encoder + decoder
# build encoder model
inputs = Input(shape=input_shape, name='encoder_input')
x = kl.Reshape((image_size_x, image_size_y, n_channels))(inputs)
x = kl.Conv2D(32, (5, 5), padding="same")(x)
#x = kl.MaxPooling2D()(x)
x = kl.Conv2D(16, (3, 3), padding="same")(x)
#x = kl.MaxPooling2D()(x)
#x = kl.Conv2D(16, (3,3), padding="same")(x)
#x = kl.MaxPooling2D()(x)
x = kl.GaussianNoise(0.001)(x)
##x = kl.Conv2D(64,(7,7), padding="same")(x)
##x = kl.Conv2D(32, (5,5), padding="same")(x)
##x = kl.Conv2D(16, (5,5), padding="same")(x)
#x = kl.Lambda(lambda y: tf.contrib.layers.spatial_softmax(y))(x)
x = kl.Flatten()(x)
#x = kl.Dropout(0.5)(x)
x = Dense(5)(x)
x = Dense(latent_dim)(x)
#x = kl.Dropout(0.2)(x)
x = Dense(image_size_x * image_size_y)(x)
z_mean = Dense(latent_dim, name='z_mean')(x)
z_log_var = Dense(latent_dim, name='z_log_var')(x)
# use reparameterization trick to push the sampling out as input
# note that "output_shape" isn't necessary with the TensorFlow backend
z = Lambda(sampling, output_shape=(latent_dim, ),
name='z')([z_mean, z_log_var])
# instantiate encoder model
encoder = Model(inputs, [z_mean, z_log_var, z], name='encoder')
# build decoder model
latent_inputs = Input(shape=(latent_dim, ), name='z_sampling')
x = Dense(intermediate_dim, activation='relu')(latent_inputs)
x = kl.Dropout(0.5)(x)
x = Dense(output_dim, activation='sigmoid')(x)
# instantiate decoder model
decoder = Model(latent_inputs, x, name='decoder')
#decoder.summary()
# instantiate VAE model
outputs = decoder(encoder(inputs)[2])
output_tensors = [z_mean, z_log_var, z]
vae = Model(inputs, outputs, name='vae_mlp')
return vae, encoder, decoder, inputs, outputs, output_tensors
def basic_Block(input, out_filters, strides=(1, 1), with_conv_shortcut=False):
x = conv3x3(input, out_filters, strides)
x = BatchNormalization(axis=3)(x)
x = kl.GaussianNoise(GNOISE)(x) #NCFC
x = Activation('relu')(x)
x = conv3x3(x, out_filters)
x = BatchNormalization(axis=3)(x)
x = kl.GaussianNoise(GNOISE)(x) #NCFC
if with_conv_shortcut:
residual = Conv2D(out_filters,
1,
strides=strides,
use_bias=False,
kernel_initializer='he_normal')(input)
residual = BatchNormalization(axis=3)(residual)
x = add([x, residual])
else:
x = add([x, input])
x = Activation('relu')(x)
return x
def tiny_model():
model = keras.Sequential()
model.add(layers.GaussianNoise(0.1, input_shape=(None, FEATURE_SIZE)))
model.add(layers.Conv1D(25, 3, activation="relu"))
model.add(layers.MaxPooling1D(3))
model.add(layers.Conv1D(50, 3, activation="relu"))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(50, activation="relu"))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(25, activation="relu"))
model.add(layers.Dropout(0.5))
model.add(
layers.Dense(max(allophone_mapping.values()) + 1,
activation="softmax"))
return model
def basic_block(y, K, args, ishape=0, residual=0, tlist=[]):
if (residual):
x = y
str = np.ones(args.autonconv)
if (residual):
str[args.autonconv - 1] = 2
str = np.int32(str)
for i in range(args.autonconv):
if (args.autocdwise == True):
y = layers.SeparableConv2D(K,
kernel_size=(3, 3),
strides=(str[i], str[i]),
padding='same')(y)
else:
y = layers.Conv2D(K,
kernel_size=(3, 3),
strides=(str[i], str[i]),
padding='same')(y)
if (args.autonobn == False):
y = layers.BatchNormalization()(y)
if (args.da_gauss != 0.0):
y = layers.GaussianNoise(0.3)(y)
if (residual == 0) | (i < args.autonconv - 1):
y = layers.ReLU()(y)
tlist.append(y)
if (residual):
if (args.autocdwise == True):
x = layers.SeparableConv2D(K,
kernel_size=(1, 1),
strides=(2, 2),
padding='same')(x)
else:
x = layers.Conv2D(K,
kernel_size=(1, 1),
strides=(2, 2),
padding='same')(x)
y = layers.add([x, y])
y = layers.ReLU()(y)
tlist.append(y)
else:
y = layers.MaxPooling2D(pool_size=(2, 2))(y)
return y
def make_model(self, obs_shape, act_shape):
if self.model_type == "neural":
input_shape = obs_shape
inputs = kl.Input(shape=input_shape, name='encoder_input')
x = kl.Dense(16)(inputs)
x = kl.Dropout(0.5)(x)
#x = kl.LSTM(10, return_sequences=True, stateful=True)(x)
x = kl.GaussianNoise(0.00001)(x)
x = kl.Dense(8)(x)
x = kl.Dropout(0.5)(x)
il_output = kl.Dense(act_shape)(x)
self.model = keras.models.Model(inputs, [il_output],
name='IL node')
self.model.compile(optimizer='adam', loss="mse")
else:
self.model = RFR(max_depth=20, criterion="mse", oob_score=True)
def auto_model(args, num_classes):
[input, x, _] = FCN(args)
x = layers.Flatten()(x)
for i in range(args.autodlayers):
x = layers.Dense(args.autodsize)(x)
if (args.autonobn == False):
x = layers.BatchNormalization()(x)
if (args.da_gauss != 0.0):
x = layers.GaussianNoise(0.3)(x)
x = layers.ReLU()(x)
x = layers.Dense(num_classes, activation='softmax')(x)
model = models.Model(inputs=[input], outputs=[x])
return model
def create_model(param: Param) -> keras.Model:
inputs = keras.Input((28, 28, 1))
x = inputs
if param.noise_stddev is not None:
x = layers.GaussianNoise(param.noise_stddev)(x)
x = layers.Lambda(lambda z: z - K.mean(z, axis=1, keepdims=True))(x)
# x = layers.Lambda(lambda z: z / K.sqrt(K.var(z, axis=1, keepdims=True)))(x)
for i in range(len(param.conv_filters)):
x = layers.Conv2D(param.conv_filters[i],
kernel_size=param.kernel_sizes[i],
strides=param.strides[i],
padding='same')(x)
x = layers.BatchNormalization(axis=-1)(x)
x = layers.ELU()(x)
if param.pool_sizes[i] is not None:
x = layers.MaxPooling2D(pool_size=param.pool_sizes[i],
strides=param.pool_strides[i])(x)
if param.conv_dropout_rates[i] is not None:
x = layers.Dropout(param.conv_dropout_rates[i])(x)
x = layers.Flatten()(x)
for units, dropout_rate in zip(param.dense_units,
param.dense_dropout_rates):
x = layers.Dense(units, activation='elu')(x)
if dropout_rate is not None:
x = layers.Dropout(dropout_rate)(x)
if param.l2_constrained_scale:
scale = param.l2_constrained_scale
x = layers.Lambda(lambda z: K.l2_normalize(z, axis=1) * scale)(x)
outputs = layers.Dense(10,
kernel_constraint=keras.constraints.UnitNorm(),
use_bias=False)(x)
else:
outputs = layers.Dense(10)(x)
model = keras.Model(inputs=inputs, outputs=outputs)
if param.center_loss_margin:
loss = CenterLoss(param.center_loss_margin)
else:
loss = tf.losses.softmax_cross_entropy
model.compile(loss=loss, optimizer='adam', metrics=['accuracy'])
return model
def get_model0(Input_img_shape=(300, 300, 3)):
def conv_bn(x, filt, dl_rate=(1, 1), preblock=False):
y = layers.Convolution2D(filt, (3, 3),
activation='linear',
padding='same',
dilation_rate=dl_rate,
use_bias=False)(x)
if preblock:
return y
y = layers.BatchNormalization()(y)
return layers.Activation('elu')(y)
#in_layer = layers.Input(t_x.shape[1:], name = 'RGB_Input')
in_layer = layers.Input(Input_img_shape, name='RGB_Input')
pp_in_layer = layers.GaussianNoise(GAUSSIAN_NOISE)(in_layer)
pp_in_layer = layers.BatchNormalization()(pp_in_layer)
c = conv_bn(pp_in_layer, BASE_DEPTH // 2)
c = conv_bn(c, BASE_DEPTH // 2)
c = conv_bn(c, BASE_DEPTH)
skip_layers = [pp_in_layer]
for j in range(BLOCK_COUNT):
depth_steps = int(np.log2(Input_img_shape[0]) - 2)
d = layers.concatenate(skip_layers + [
conv_bn(c, BASE_DEPTH * 2**j, (2**i, 2**i), preblock=True)
for i in range(depth_steps)
])
d = layers.SpatialDropout2D(SPATIAL_DROPOUT)(d)
d = layers.BatchNormalization()(d)
d = layers.Activation('elu')(d)
# bottleneck
d = conv_bn(d, BASE_DEPTH * 2**(j + 1))
skip_layers += [c]
c = d
d = layers.Convolution2D(1, (1, 1), activation='sigmoid',
padding='same')(d)
d = layers.Cropping2D((EDGE_CROP, EDGE_CROP))(d)
d = layers.ZeroPadding2D((EDGE_CROP, EDGE_CROP))(d)
seg_model = models.Model(inputs=[in_layer], outputs=[d])
#seg_model.summary()
return seg_model
def discriminator_network(x):
def add_common_layers(y):
y = layers.advanced_activations.LeakyReLU()(y)
y = layers.Dropout(0.25)(y)
return y
x = layers.GaussianNoise(stddev=0.2)(x)
x = layers.Conv2D(64, kernel_size, **conv_layer_keyword_args)(x)
x = add_common_layers(x)
x = layers.Conv2D(128, kernel_size, **conv_layer_keyword_args)(x)
x = add_common_layers(x)
x = layers.Flatten()(x)
x = layers.Dense(1024)(x)
x = add_common_layers(x)
return layers.Dense(1, activation='sigmoid')(x)
