def triplet_loss(y_true, y_pred, alpha=0.4):
"""
Implementation of the triplet loss function
Arguments:
y_true -- true labels, required when you define a loss in Keras, you don't need it in this function.
y_pred -- python list containing three objects:
anchor -- the encodings for the anchor data
positive -- the encodings for the positive data (similar to anchor)
negative -- the encodings for the negative data (different from anchor)
Returns:
loss -- real number, value of the loss
"""
print('y_pred.shape = ', y_pred)
total_lenght = y_pred.shape.as_list()[-1]
# print('total_lenght=', total_lenght)
# total_lenght =12
anchor = y_pred[:, 0:int(total_lenght * 1 / 3)]
positive = y_pred[:, int(total_lenght * 1 / 3):int(total_lenght * 2 / 3)]
negative = y_pred[:, int(total_lenght * 2 / 3):int(total_lenght * 3 / 3)]
# distance between the anchor and the positive
pos_dist = K.sum(K.square(anchor - positive), axis=1)
# distance between the anchor and the negative
neg_dist = K.sum(K.square(anchor - negative), axis=1)
# compute loss
basic_loss = pos_dist - neg_dist + alpha
loss = K.maximum(basic_loss, 0.0)
return loss
def correlation_coefficient_loss(y_true, y_pred):
x = y_true
y = y_pred
mx = K.mean(x)
my = K.mean(y)
xm, ym = x-mx, y-my
r_num = K.sum(tf.multiply(xm,ym))
r_den = K.sqrt(tf.multiply(K.sum(K.square(xm)), K.sum(K.square(ym))))
r = r_num / r_den
r = K.maximum(K.minimum(r, 1.0), -1.0)
return K.square(r)
def weighted_mse(y_pred, y_true):
majority_weight = 0.9500
minority_weight = 0.0500
# Apply the weights
loss = K.mean(K.square((y_pred - y_true) * (y_true * majority_weight) +
(1. - y_true) * minority_weight),
axis=-1)
# Return the mean error
return loss
def vae_loss(self, y_true, y_pred):
""" Calculate loss = reconstruction loss + KL loss for eatch data in minibatch """
# E[log P(X|z)]
recon = K.sum(K.sum(K.binary_crossentropy(y_pred, y_true), axis=1))
recon *= 256
# D_KL(Q(z|X) || P(z|X)); calculate in closed from as both dist. are Gaussian
kl = 0.5 * K.sum(
K.sum(K.exp(self.log_sigma) + K.square(self.mu) - 1. -
self.log_sigma,
axis=1))
return recon + kl
# def sample_z(self,mu,log_sigma):
# #mu, log_sigma = args
# eps = K.backend.random_normal(shape=(self.mini_batch_size, self.latent_space_dim), mean=0., stddev=1.)
# temp=np.array(log_sigma )/ 2.
# return mu + K.exp(temp) * eps
def deform_center_cnn(class_num, trainable=False, GPU=1):
conv_args = {
'trainable': trainable,
'kernel_initializer': Orthogonal(gain=1.0, seed=None),
'kernel_regularizer': OrthLocalReg2D,
'padding': 'same'
}
sep_conv_args = {
'trainable': trainable,
'kernel_initializer': Orthogonal(gain=1.0, seed=None),
'kernel_regularizer': OrthLocalRegSep2D,
'padding': 'same'
}
inputs = l = Input((None, None, 3), name='input')
input_target = Input((1, ), name='input_target')
# norm_input = RGB2Gray()(inputs)
norm_input = ImageNorm()(inputs)
# norm_input = inputs
stem_stride = (2, 2)
# conv11
l = Conv2D(32, (3, 3), strides=stem_stride, name='conv11',
**conv_args)(norm_input)
l = Activation('relu', name='conv11_relu')(l)
l = BatchNormalization(name='conv11_bn')(l)
l2 = InvConv2D(32, (3, 3),
strides=stem_stride,
name='inv_conv11',
**conv_args)(norm_input)
l2 = Activation('relu', name='inv_conv11_relu')(l2)
l2 = BatchNormalization(name='inv_conv11_bn')(l2)
l = concatenate([l, l2])
l5 = SeparableConv2D(64, (5, 5),
strides=(2, 2),
name='conv5_11_12',
**sep_conv_args)(l)
l5 = Activation('relu', name='conv5_11_12_relu')(l5)
l5 = BatchNormalization(name='conv5_11_12_bn')(l5)
l3 = SeparableConv2D(64, (3, 3),
strides=(2, 2),
name='conv3_11_12',
**sep_conv_args)(l)
l3 = Activation('relu', name='conv3_11_12_relu')(l3)
l3 = BatchNormalization(name='conv3_11_12_bn')(l3)
l = concatenate([l3, l5])
l = SeparableConv2D(128, (3, 3), name='conv12_1', **sep_conv_args)(l)
l = Activation('relu', name='conv12_1_relu')(l)
l = BatchNormalization(name='conv12_1_bn')(l)
l = SeparableConv2D(128, (1, 1), name='conv12_2', **sep_conv_args)(l)
l = Activation('relu', name='conv12_2_relu')(l)
l = BatchNormalization(name='conv12_2_bn')(l)
l = SeparableConv2D(128, (3, 3), name='conv13', **sep_conv_args)(l)
l = Activation('relu', name='conv13_relu')(l)
l = BatchNormalization(name='conv13_bn')(l)
l = SeparableConv2D(256, (3, 3),
strides=(2, 2),
name='conv14',
**sep_conv_args)(l)
l = Activation('relu', name='conv14_relu')(l)
l = l14 = BatchNormalization(name='conv14_bn')(l)
l = SeparableConv2D(256, (3, 3), name='conv21', **sep_conv_args)(l)
l = Activation('relu', name='conv21_relu')(l)
l = BatchNormalization(name='conv21_bn')(l)
l = SeparableConv2D(256, (3, 3), name='conv22', **sep_conv_args)(l)
l = Activation('relu', name='conv22_relu')(l)
l = BatchNormalization(name='conv22_bn')(l)
l = Add(name='residual_14_22')([l14, l])
# conv22
# l_offset = ConvOffset2D(192, name='conv32_offset')(l)
# l = Conv2D(256, (3, 3), strides=(2, 2), name='conv23', **conv_args)(l)
# l = Activation('relu', name='conv23_relu')(l)
# l = l23 = BatchNormalization(name='conv23_bn')(l)
#
# l = Conv2D(256, (3, 3), name='conv31', **conv_args)(l)
# l = Activation('relu', name='conv31_relu')(l)
# l = BatchNormalization(name='conv31_bn')(l)
# l = Conv2D(256, (1, 1), name='conv32', **conv_args)(l31)
# l = Activation('relu', name='conv32_relu')(l)
# l = BatchNormalization(name='conv32_bn')(l)
# l = Conv2D(256, (3, 3), name='conv33', **conv_args)(l)
# l = Activation('relu', name='conv33_relu')(l)
# l = BatchNormalization(name='conv33_bn')(l)
# l = Add(name='residual_23_33')([l23, l])
l_offset = ConvOffset2D(256, name='conv33_offset')(l)
l = SeparableConv2D(512, (3, 3),
strides=(2, 2),
name='conv41',
**sep_conv_args)(l_offset)
l = Activation('relu', name='conv41_relu')(l)
l = BatchNormalization(name='conv41_bn')(l)
l = SeparableConv2D(512, (3, 3), name='conv42', **sep_conv_args)(l)
l = Activation('relu', name='conv42_relu')(l)
l = BatchNormalization(name='conv42_bn')(l)
# l_offset = ConvOffset2D(512, name='conv35_offset')(l)
l = SeparableConv2D(512, (3, 3), name='conv43', **sep_conv_args)(l)
l = Activation('relu', name='conv43_relu')(l)
l = BatchNormalization(name='conv43_bn')(l)
# l = LocallyConnected2D(512, (3, 3), name='conv44', padding='valid')(l)
# l = Activation('relu', name='conv44_relu')(l)
# l = BatchNormalization(name='conv44_bn')(l)
l = SeparableConv2D(1024, (3, 3),
strides=(2, 2),
name='conv51',
**sep_conv_args)(l)
l = Activation('relu', name='conv51_relu')(l)
l = BatchNormalization(name='conv51_bn')(l)
# l_offset = ConvOffset2D(1024, name='conv35_offset')(l)
l = SeparableConv2D(1024, (3, 3), name='conv52', **sep_conv_args)(l)
l = Activation('relu', name='conv52_relu')(l)
l = BatchNormalization(name='conv52_bn')(l)
l = SeparableConv2D(1024, (3, 3), name='conv53', **sep_conv_args)(l)
l = Activation('relu', name='conv53_relu')(l)
l = BatchNormalization(name='conv53_bn')(l)
# out
l = GlobalAvgPool2D(name='avg_pool')(l)
# l = MaxPooling2D(name='max_pool_final')(l)
# l = Flatten(name='flatten_maxpool')(l)
l = Dense(512, name='fc1', trainable=trainable)(l)
l = Activation('relu', name='fc1_relu')(l)
l = Dense(256, name='fc2', trainable=trainable)(l)
l = feature = Activation('relu', name='fc2_relu')(l)
l = Dense(class_num, name='fc3', trainable=trainable)(l)
outputs = l = Activation('softmax', name='out')(l)
if GPU == 1:
centers = Embedding(class_num, 256)(input_target)
l2_loss = Lambda(
lambda x: K.sum(K.square(x[0] - x[1][:, 0]), 1, keepdims=True),
name='l2_loss')([feature, centers])
Model(inputs=[inputs, input_target], outputs=[outputs, l2_loss])
elif GPU == 0:
return inputs, outputs
else:
BODY = Model(inputs=[inputs, input_target], outputs=[outputs, feature])
BODY = make_parallel(BODY, GPU)
softmax_output = Lambda(lambda x: x, name='output')(BODY.outputs[0])
centers = Embedding(class_num, 256)(input_target)
l2_loss = Lambda(
lambda x: K.sum(K.square(x[0] - x[1][:, 0]), 1, keepdims=True),
name='l2_loss')([BODY.outputs[1], centers])
model_withcneter = Model(inputs=BODY.inputs,
outputs=[softmax_output, l2_loss])
return model_withcneter
def act(state) #roll the dice for a move
if random.random() < epsilon
return random action
else
return optimal action # action = argmax Q(s, a')
def observe()
observe that reward
def remember()
def learn() #learning fun
def loss(target, prediction)
err = prediction - target
loss = keras.mean(keras.sqrt(1 + keras.square(error)) - 1)
return (loss)
def _build_model(self):
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=self._huber_loss, optimizer=Adam(lr=self.learning_rate))
return model
def load(self, name):
self.model.load_weights(name)
def save(self, name):
self.model.save_weights(name)
def get_large_deform_cnn2(class_num, trainable=False, GPU=1):
# init = Orthogonal(gain=1.0, seed=None)
init = 'random_normal'
inputs = l = Input((200, 200, 3), name='input')
input_target = Input((1, ), name='input_target')
#norm_input = ImageNorm()(inputs)
norm_input = inputs
# conv11
l = Conv2D(32, (3, 3),
padding='same',
name='conv11',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(norm_input)
l = Activation('relu', name='conv11_relu')(l)
l = BatchNormalization(name='conv11_bn')(l)
l2 = InvConv2D(32, (3, 3),
padding='same',
name='inv_conv11',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(norm_input)
l2 = Activation('relu', name='inv_conv11_relu')(l2)
l2 = BatchNormalization(name='inv_conv11_bn')(l2)
l3 = Conv2D(32, (3, 1),
padding='same',
name='conv11_2',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(norm_input)
l3 = Activation('relu', name='conv11_2_relu')(l3)
l3 = BatchNormalization(name='conv11_2_bn')(l3)
l5 = Conv2D(32, (1, 3),
padding='same',
name='conv11_3',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(norm_input)
l5 = Activation('relu', name='conv11_3_relu')(l5)
l5 = BatchNormalization(name='conv11_3_bn')(l5)
l4 = InvConv2D(32, (3, 1),
padding='same',
name='conv11_2i',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(norm_input)
l4 = Activation('relu', name='conv11_2i_relu')(l4)
l4 = BatchNormalization(name='conv11_2i_bn')(l4)
l6 = InvConv2D(32, (1, 3),
padding='same',
name='conv11_3i',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(norm_input)
l6 = Activation('relu', name='conv11_3i_relu')(l6)
l6 = BatchNormalization(name='conv11_3i_bn')(l6)
l = concatenate([l, l2, l3, l5, l4, l6])
# conv12
# l_offset = ConvOffset2D(32, name='conv12_offset')(l)
l5 = Conv2D(128, (5, 5),
padding='same',
strides=(2, 2),
name='pool5_11_12',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l5 = Activation('relu', name='pool5_11_12_relu')(l5)
l5 = BatchNormalization(name='pool5_11_12_bn')(l5)
l3 = Conv2D(128, (3, 3),
padding='same',
strides=(2, 2),
name='pool3_11_12',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l3 = Activation('relu', name='pool3_11_12_relu')(l3)
l3 = BatchNormalization(name='pool3_11_12_bn')(l3)
l = concatenate([l3, l5])
l3 = Conv2D(32, (5, 3),
padding='same',
name='conv12_2',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l3 = Activation('relu', name='conv12_2_relu')(l3)
l3 = BatchNormalization(name='conv12_2_bn')(l3)
l5 = Conv2D(32, (3, 5),
padding='same',
name='conv12_3',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l5 = Activation('relu', name='conv12_3_relu')(l5)
l5 = BatchNormalization(name='conv12_3_bn')(l5)
l = Conv2D(128, (3, 3),
padding='same',
name='conv12',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l = Activation('relu', name='conv12_relu')(l)
l = BatchNormalization(name='conv12_bn')(l)
l = concatenate([l, l3, l5])
l = Conv2D(128, (3, 3),
padding='same',
name='conv13',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l = Activation('relu', name='conv13_relu')(l)
l = jump = BatchNormalization(name='conv13_bn')(l)
l = Conv2D(192, (3, 3),
padding='same',
strides=(2, 2),
name='conv14',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l = Activation('relu', name='conv14_relu')(l)
# l = BatchNormalization(name='conv14_bn')(l)
l = Conv2D(192, (3, 3),
padding='same',
name='conv21',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l = Activation('relu', name='conv21_relu')(l)
l = BatchNormalization(name='conv21_bn')(l)
l = Conv2D(192, (3, 3),
padding='same',
name='conv22',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l = Activation('relu', name='conv22_relu')(l)
l = BatchNormalization(name='conv22_bn')(l)
# conv22
# l_offset = ConvOffset2D(192, name='conv32_offset')(l)
l = Conv2D(256, (3, 3),
padding='same',
strides=(2, 2),
name='conv23',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l = Activation('relu', name='conv23_relu')(l)
l = BatchNormalization(name='conv23_bn')(l)
l = Conv2D(256, (3, 3),
padding='same',
name='conv31',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l = Activation('relu', name='conv31_relu')(l)
l31 = BatchNormalization(name='conv31_bn')(l)
# l = Conv2D(256, (1, 1), padding='same', name='conv32', trainable=trainable, kernel_initializer=init, kernel_regularizer=OrthLocalReg2D)(l31)
# l = Activation('relu', name='conv32_relu')(l)
# l = BatchNormalization(name='conv32_bn')(l)
l = Conv2D(256, (3, 3),
padding='same',
name='conv33',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l = Activation('relu', name='conv33_relu')(l)
l = BatchNormalization(name='conv33_bn')(l)
l = Add(name='residual_31_32')([l31, l])
lj = Conv2D(256, (3, 3),
padding='same',
strides=(2, 2),
name='jump_pool',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(jump)
lj = Activation('relu', name='jump_pool_relu')(lj)
lj = BatchNormalization(name='jump_pool_bn')(lj)
lj = Conv2D(256, (3, 3),
padding='same',
strides=(2, 2),
name='jump_pool2',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(lj)
lj = Activation('relu', name='jump_pool2_relu')(lj)
lj = BatchNormalization(name='jump_pool2_bn')(lj)
l = concatenate([l, lj])
l_offset = ConvOffset2D(512, name='conv33_offset')(l)
l = Conv2D(512, (3, 3),
padding='same',
strides=(2, 2),
name='conv41',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l_offset)
l = Activation('relu', name='conv41_relu')(l)
l = BatchNormalization(name='conv41_bn')(l)
l = Conv2D(512, (3, 3),
padding='same',
name='conv42',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l = Activation('relu', name='conv42_relu')(l)
l = BatchNormalization(name='conv42_bn')(l)
# l_offset = ConvOffset2D(512, name='conv35_offset')(l)
l = Conv2D(512, (3, 3),
padding='same',
name='conv43',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg2D)(l)
l = Activation('relu', name='conv43_relu')(l)
l = BatchNormalization(name='conv43_bn')(l)
# out
# l = GlobalAvgPool2D(name='avg_pool')(l)
l = MaxPooling2D(name='max_pool_final')(l)
l = Flatten(name='flatten_maxpool')(l)
l = Dense(768,
name='fc1',
trainable=trainable,
kernel_initializer=init,
kernel_regularizer=OrthLocalReg1D)(l)
l = Activation('relu', name='fc1_relu')(l)
l = feature = Dense(256,
name='fc2',
trainable=trainable,
kernel_initializer=init)(l)
l = Activation('relu', name='fc2_relu')(l)
l = Dense(class_num, name='fc3', trainable=trainable)(l)
outputs = l = Activation('softmax', name='out')(l)
if GPU == 1:
centers = Embedding(class_num, 256)(input_target)
l2_loss = Lambda(
lambda x: K.sum(K.square(x[0] - x[1][:, 0]), 1, keepdims=True),
name='l2_loss')([feature, centers])
Model(inputs=[inputs, input_target], outputs=[outputs, l2_loss])
elif GPU == 0:
return inputs, outputs
else:
BODY = Model(inputs=[inputs, input_target], outputs=[outputs, feature])
BODY = make_parallel(BODY, GPU)
softmax_output = Lambda(lambda x: x, name='output')(BODY.outputs[0])
centers = Embedding(class_num, 256)(input_target)
l2_loss = Lambda(
lambda x: K.sum(K.square(x[0] - x[1][:, 0]), 1, keepdims=True),
name='l2_loss')([BODY.outputs[1], centers])
model_withcneter = Model(inputs=BODY.inputs,
outputs=[softmax_output, l2_loss])
return model_withcneter
def root_mean_squared_error(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))
def rmse(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true)))
def custom_loss(y_true, y_pred):
return keras.mean(keras.square(func_g(X[-1])-y_pred))
def loss(y_true, y_pred):
return k.mean(k.square(y_pred - y_true) - k.square(y_true), axis=-1)
# we instantiate these layers separately so as to reuse them later
decoder_f = Dense(dense2, activation='tanh')
decoder_h = Dense(dense1, activation='tanh')
decoder_mean = Dense(original_dim, activation='tanh')
f_decoded = decoder_f(z)
h_decoded = decoder_h(f_decoded)
x_decoded_mean = decoder_mean(h_decoded)
x_decoded_img = Reshape(original_shape)(x_decoded_mean)
# instantiate VAE model
vae = Model(in_layer, x_decoded_img)
# Compute VAE loss
xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)
kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var),
axis=-1)
vae_loss = K.mean(0.5 * xent_loss + 0.5 * kl_loss)
vae.add_loss(vae_loss)
vae.compile(optimizer='adam')
vae.summary()
vae.fit(x_train,
shuffle=True,
epochs=train_epoch,
batch_size=batch_size,
validation_data=(anomaly_test, None))
vae.save('%s = %d %d vae_dense2_model.hdf5' % (var_str, var1, var2))