def model_instance_master(channels, loss_rate, clip_norm):
input_img = Input((None, None, channels))
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(input_img)
# padding = 'same' should be zero padding
for ii in range(0, 18):
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(1, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
res_img = model
output_img = add([res_img, input_img])
model = Model(input_img, output_img)
adam = Adam(lr=loss_rate, clipnorm=clip_norm)
# loss function is an input argument but MSE seems to generally work well
model.compile(optimizer=adam,
loss='mean_squared_error',
metrics=['mean_squared_error'])
return model
def residual_TCN_LSTM(n_nodes,
conv_len,
n_classes,
n_feat,
max_len,
loss='categorical_crossentropy',
online=False,
optimizer="rmsprop",
depth=3,
return_param_str=False):
n_layers = len(n_nodes)
inputs = Input(shape=(max_len, n_feat))
model = inputs
prev = Conv1D(n_nodes[0], conv_len, padding='same')(model)
# encoder
for i in range(n_layers):
for j in range(depth):
# convolution over the temporal dimension
current = encoder_identify_block(prev, n_nodes[i], conv_len)
# residual connection within residual block
if j != 0:
model = add([prev, current])
model = Activation('relu')(model)
prev = current
if i < (n_layers - 1):
model = MaxPooling1D(2)(model)
# decoder
# for i in range(n_layers):
#
# for j in range(depth):
#
# current = decoder_identify_block(model, n_nodes[-i - 1])
# model = add([prev, current])
# prev = current
# model = UpSampling1D(2)(model)
# Output FC layer
model = TimeDistributed(Dense(n_classes, activation="softmax"))(model)
model = Model(inputs=inputs, outputs=model)
model.compile(loss=loss,
optimizer=optimizer,
sample_weight_mode="temporal",
metrics=['accuracy'])
model.summary()
if return_param_str:
param_str = "ED-TCN_C{}_L{}".format(conv_len, n_layers)
if online:
param_str += "_online"
return model, param_str
else:
return model
def build_model(self):
inputs = Input(shape=(self.img_dims[0], self.img_dims[1],
self.num_consecutive_frames))
model = Conv2D(32, (8, 8), strides=(4, 4), padding='valid')(inputs)
model = Activation('relu')(model)
model = Conv2D(64, (4, 4), strides=(2, 2), padding='valid')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), strides=(1, 1), padding='valid')(model)
model = Activation('relu')(model)
model = Flatten()(model)
model = Dense(512)(model)
model = Activation('relu')(model)
model = Dense(self.num_actions)(model)
model = Model(inputs=inputs, outputs=model)
adam = Adam(lr=self.learning_rate)
model.compile(loss='mse', optimizer=adam)
print("We finish building the model")
self.model = model
def build_model(self):
inputs = Input(shape=(self.img_dims[0], self.img_dims[1],
self.num_consecutive_frames))
model = Conv2D(32, (8, 8), strides=(4, 4), padding='valid')(inputs)
model = Activation('relu')(model)
model = Conv2D(64, (4, 4), strides=(2, 2), padding='valid')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), strides=(1, 1), padding='valid')(model)
model = Activation('relu')(model)
stream = Flatten()(model)
advantage = Dense(self.num_actions, name='Advantage_stream')(stream)
value = Dense(1, name='Value_stream')(stream)
def mean(x):
import keras.backend
res = keras.backend.mean(x, axis=1, keepdims=True)
return res
meanRes = Lambda(function=mean, name='Mean_layer')(advantage)
from keras.layers import Concatenate
concatenations = []
for i in range(self.num_actions):
concatenations.append(meanRes)
meanRes = Concatenate(name='Mean_broadcasting')(concatenations)
advantage = keras.layers.subtract([advantage, meanRes], name='A_z')
qOut = keras.layers.add([value, advantage], name='Q_out')
model = Model(inputs=inputs, outputs=qOut)
adam = Adam(lr=self.learning_rate)
model.compile(loss='mse', optimizer=adam)
print("We finish building the model")
self.model = model
self.target_model = Agent.copy_model(self.model)
def model_train(img_size,
batch_size,
epochs,
optimizer,
learning_rate,
train_list,
validation_list,
style=2):
print('Style {}.'.format(style))
if style == 1:
input_img = Input(shape=img_size)
#model = Sequential()
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal',
input_shape=img_size)(input_img)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(1, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
res_img = model
output_img = merge.Add()([res_img, input_img])
model = Model(input_img, output_img)
#model.load_weights('vdsr_model_edges.h5')
adam = Adam(lr=0.000005)
#sgd = SGD(lr=1e-3, momentum=0.9, decay=1e-4, nesterov=False)
sgd = SGD(lr=0.01, momentum=0.9, decay=0.001, nesterov=False)
#model.compile(sgd, loss='mse', metrics=[PSNR, "accuracy"])
model.compile(adam,
loss='mse',
metrics=[ssim, ssim_metric, PSNR, "accuracy"])
model.summary()
else:
input_img = Input(shape=img_size)
model = Conv2D(64, (3, 3),
padding='valid',
kernel_initializer='he_normal')(input_img)
model_0 = Activation('relu')(model)
total_conv = 22 # should be even number
total_conv -= 2 # subtract first and last
residual_block_num = 5 # should be even number
for _ in range(residual_block_num): # residual block
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model_0)
model = Activation('relu')(model)
for _ in range(int(total_conv / residual_block_num) - 1):
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model_0 = add([model, model_0])
model = Conv2D(1, (3, 3),
padding='valid',
kernel_initializer='he_normal')(model)
res_img = model
input_img1 = crop(1, 2, -2)(input_img)
input_img1 = crop(2, 2, -2)(input_img1)
print(input_img.shape)
print(input_img1.shape)
output_img = merge.Add()([res_img, input_img1])
# output_img = res_img
model = Model(input_img, output_img)
# model.load_weights('./vdsr_model_edges.h5')
# adam = Adam(lr=learning_rate)
adam = Adadelta()
# sgd = SGD(lr=1e-7, momentum=0.9, decay=1e-2, nesterov=False)
sgd = SGD(lr=learning_rate,
momentum=0.9,
decay=1e-4,
nesterov=False,
clipnorm=1)
if optimizer == 0:
model.compile(adam, loss='mse', metrics=[ssim, ssim_metric, PSNR])
else:
model.compile(sgd, loss='mse', metrics=[ssim, ssim_metric, PSNR])
model.summary()
mycallback = MyCallback(model)
timestamp = time.strftime("%m%d-%H%M", time.localtime(time.time()))
csv_logger = callbacks.CSVLogger(
'data/callbacks/training_{}.log'.format(timestamp))
filepath = "./checkpoints/weights-improvement-{epoch:03d}-{PSNR:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor=PSNR, verbose=1, mode='max')
callbacks_list = [mycallback, checkpoint, csv_logger]
# print('Loading training data.')
# x = load_images(DATA_PATH)
# print('Loading data label.')
# y = load_images(LABEL_PATH)
# print('Loading validation data.')
# val = load_images(VAL_PATH)
# print('Loading validation label.')
# val_label = load_images(VAL_LABEL_PATH)
# print(x.shape)
# print(y.shape)
# print(val.shape)
# print(val_label.shape)
with open('./model/vdsr_architecture.json', 'w') as f:
f.write(model.to_json())
# datagen = ImageDataGenerator(rotation_range=45,
# zoom_range=0.15,
# horizontal_flip=True,
# vertical_flip=True)
# history = model.fit_generator(datagen.flow(x, y, batch_size=batch_size),
# steps_per_epoch=len(x) // batch_size,
# validation_data=(val, val_label),
# validation_steps=len(val) // batch_size,
# epochs=epochs,
# callbacks=callbacks_list,
# verbose=1,
# shuffle=True,
# workers=256,
# use_multiprocessing=True)
history = model.fit_generator(
image_gen(train_list, batch_size=batch_size),
steps_per_epoch=384400 * (len(train_list)) // batch_size,
# steps_per_epoch=4612800//batch_size,
validation_data=image_gen(validation_list, batch_size=batch_size),
validation_steps=384400 * (len(validation_list)) // batch_size,
epochs=epochs,
workers=1024,
callbacks=callbacks_list,
verbose=1)
print("Done training!!!")
print("Saving the final model ...")
model.save('vdsr_model.h5') # creates a HDF5 file
del model # deletes the existing model
# plt.plot(history.history['accuracy'])
# plt.plot(history.history['val_accuracy'])
# plt.title('Model accuracy')
# plt.ylabel('Accuracy')
# plt.xlabel('Epoch')
# plt.legend(['Train', 'validation'], loc='upper left')
# # plt.show()
# plt.savefig('accuracy.png')
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'validation'], loc='upper left')
# plt.show()
plt.savefig('loss.png')
plt.plot(history.history['PSNR'])
plt.plot(history.history['val_PSNR'])
plt.title('Model PSNR')
plt.ylabel('PSNR')
plt.xlabel('Epoch')
plt.legend(['Train', 'validation'], loc='upper left')
# plt.show()
plt.savefig('PSNR.png')
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(1, (3, 3), padding='same', kernel_initializer='he_normal')(model)
res_img = model
output_img = add([res_img, input_img])
model = Model(input_img, output_img)
# model.load_weights('./checkpoints/weights-improvement-20-26.93.hdf5')
adam = Adam(lr=0.001, decay=1e-5, clipvalue=0.1, epsilon=1e-8)
sgd = SGD(lr=1e-2, momentum=0.9, decay=1e-4, nesterov=False)
model.compile(adam, loss='mse', metrics=[PSNR, "accuracy"])
model.summary()
# 每个epoch保存一次模型
filepath = "./checkpoints2/vdsr-{epoch:02d}-{PSNR:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor=PSNR, verbose=1, mode='max')
callbacks_list = [checkpoint]
# 记录每次训练的loss，PSNR值
train_log = CSVLogger(filename="train.log")
model.fit(x=train_input, y=train_label, batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=callbacks_list, shuffle=True)
print("Done training!!!")
def ED_TCN(n_nodes,
conv_len,
n_classes,
n_feat,
max_len,
loss='categorical_crossentropy',
online=False,
optimizer="rmsprop",
activation='norm_relu',
attention=True,
return_param_str=False):
n_layers = len(n_nodes)
# inputs = Input(shape=(max_len, n_feat))
inputs = Input(shape=(None, n_feat))
# attention layer, apply weightings to input
if attention:
model = attention_block(inputs, 100)
else:
model = inputs
# ---- Encoder ----
for i in range(n_layers):
# Pad beginning of sequence to prevent usage of future data
if online: model = ZeroPadding1D((conv_len // 2, 0))(model)
# convolution over the temporal dimension
model = Conv1D(n_nodes[i], conv_len, padding='same')(model)
if online: model = Cropping1D((0, conv_len // 2))(model)
model = SpatialDropout1D(0.3)(model)
if activation == 'norm_relu':
model = Activation('relu')(model)
model = Lambda(channel_normalization,
name="encoder_norm_{}".format(i))(model)
elif activation == 'wavenet':
model = WaveNet_activation(model)
else:
model = Activation(activation)(model)
# hidden features layer when in the last interation
model = MaxPooling1D(2)(model)
# ---- Decoder ----
for i in range(n_layers):
model = UpSampling1D(2)(model)
if online: model = ZeroPadding1D((conv_len // 2, 0))(model)
model = Conv1D(n_nodes[-i - 1], conv_len, padding='same')(model)
if online: model = Cropping1D((0, conv_len // 2))(model)
model = SpatialDropout1D(0.3)(model)
if activation == 'norm_relu':
model = Activation('relu')(model)
model = Lambda(channel_normalization,
name="decoder_norm_{}".format(i))(model)
elif activation == 'wavenet':
model = WaveNet_activation(model)
else:
model = Activation(activation)(model)
# Output FC layer
model = TimeDistributed(Dense(n_classes, activation="softmax"))(model)
model = Model(inputs=inputs, outputs=model)
model.compile(loss=loss,
optimizer=optimizer,
sample_weight_mode="temporal",
metrics=['accuracy'])
model.summary()
if return_param_str:
param_str = "ED-TCN_C{}_L{}".format(conv_len, n_layers)
if online:
param_str += "_online"
return model, param_str
else:
return model
def define_model_weighted(structure, filters, dropouts, init_shape, weight_decay, num_classes, weight = 1., temperature = 1):
"""
This model is a modified version of the previous one. The only difference is that this has two
outputs: one with soft labels depending on the temperature (same as previous model) and an
additional output with the hard labels (softmax with temperature 1). The soft labels are trained
by distillation from the complex network and the hard labels with the real labels from the dataset.
Cross-entropy loss for both outputs but with different weights, Temperature-normalized.
Small pack: conv2d -> elu -> BN
Big Pack: at least 1 small pack + maxPool + DropOut
:param structure: list of integers. Each number indicates one "Big Pack", and the number itself
the number of "small packs" in the "Big Pack". Strcture = [2,2,2] is the one of the complex network
:param filters: list of integers, number of filters in the conv2d of each Big Pack.
filters = [32, 64, 128] for the complex network
:param dropouts: drop-out ratio for the drop out layer at the end of each Big Pack. for the
complex network dropouts = [0.2, 0.3, 0.4]
:return: model
"""
# Check that the input of the function has sense
assert len(structure)==len(filters)==len(dropouts), "The length of the inputs don't match"
assert min(structure) > 0, "All the Big Packs should include at least one Small Pack"
assert min(dropouts) > 0 or max(dropouts) < 1, "Drop outs should be between 0 an 1"
# Definition of the network, Functional API in order to handle multiple outputs
inp = Input(init_shape)
for i, number_small_pack in enumerate(structure):
for j in range(number_small_pack):
if i + j == 0:
model = Conv2D(filters[i], (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))(inp)
else:
model = Conv2D(filters[i], (3, 3), padding='same',
kernel_regularizer=regularizers.l2(weight_decay))(model)
model = Activation('elu')(model)
model = BatchNormalization()(model)
model = MaxPooling2D(pool_size=(2,2))(model)
model = Dropout(dropouts[i])(model)
model = Flatten()(model)
dense = Dense(num_classes)(model)
out_hard = Activation('softmax', name = 'out_hard')(dense)
out_soft = Lambda(lambda x: x / temperature)(dense)
out_soft = Activation('softmax', name = 'out_soft')(out_soft)
model = Model(inputs = inp, outputs = [out_hard, out_soft])
# handle multiple losses and weights
losses = {
"out_hard": "categorical_crossentropy",
"out_soft": "categorical_crossentropy",
}
lossWeights = {"out_soft": temperature**2, "out_hard": weight}
model.compile(loss=losses, loss_weights = lossWeights, optimizer='adam', metrics=['accuracy'])
return model
model.fit(X_train, y[train_idx], batch_size=cm.bs, epochs=cm.n_ep,
verbose=0, callbacks=[cm.custom_stopping(value=cm.loss, verbose=2)],
validation_data=(X_train, y[train_idx]))
hyper_net = LatentHyperNet(n_comp=19, model=model, layers=layers, dm_method='pls')
hyper_net.fit(X_train, y[train_idx])
X_train = hyper_net.transform(X_train)
X_test = hyper_net.transform(X_test)
inp = Input((X_train.shape[1],))
fc = Dense(n_class)(inp)
model = Activation('softmax')(fc)
model = Model(inp, model)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='Adadelta')
callbacks = [cm.custom_stopping(value=cm.loss, verbose=2)]
model.fit(X_train, y[train_idx], batch_size=len(X_train),
epochs=4*cm.n_ep,#The drawback of the method is that it requires more iterations to converge (loss <= cm.loss)
verbose=0, callbacks=callbacks, validation_data=(X_train, y[train_idx]))
y_pred = model.predict(X_test)
y_pred = np.argmax(y_pred, axis=1)
y_true = np.argmax(y[test_idx], axis=1)
acc_fold = accuracy_score(y_true, y_pred)
avg_acc.append(acc_fold)
recall_fold = recall_score(y_true, y_pred, average='macro')
activation='relu',
kernel_initializer='he_normal')(model)
model = Conv2D(1, (1, 1), padding='same',
kernel_initializer='he_normal')(model)
res_img = model
#Skip Connection
output_img = add([res_img, input_img])
model = Model(input_img, output_img)
#model.load_weights('./Saved Models/sr_PBNet_model_with200epoch')
adam = Adam(lr=0.001, decay=1e-4)
sgd = SGD(lr=1e-5, momentum=0.9, decay=1e-4, nesterov=False)
#custom_loss = mae_mssim_loss(alpha=0.8)
model.compile(adam, loss='mae', metrics=[PSNR, SSIM])
model.summary()
filepath = "./saved_weights/weights-improvement-{epoch:02d}-{val_PSNR:.2f}.h5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_PSNR',
verbose=1,
save_best_only=True,
save_weights_only=True,
mode='max')
lrate = LearningRateScheduler(step_decay)
callbacks_list = [checkpoint, lrate]
print("Started training")
history = model.fit_generator(image_gen(train_list), steps_per_epoch=len(train_list) // BATCH_SIZE, \
validation_data=image_gen(test_list), validation_steps=len(test_list) // BATCH_SIZE,
epochs=EPOCHS, workers=32, callbacks=callbacks_list, verbose=1)
def model_train(img_size, batch_size, epochs, optimizer, learning_rate, train_list, validation_list, style=2):
print('Style {}.'.format(style))
if style == 1:
input_img = Input(shape=img_size)
#model = Sequential()
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', input_shape=img_size)(input_img)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(1, (3, 3), padding='same', kernel_initializer='he_normal')(model)
res_img = model
output_img = merge.Add()([res_img, input_img])
model = Model(input_img, output_img)
#model.load_weights('vdsr_model_edges.h5')
adam = Adam(lr=0.000005)
sgd = SGD(lr=0.01, momentum=0.9, decay=0.001, nesterov=False)
model.compile(adam, loss='mse', metrics=[ssim, ssim_metric, PSNR, "accuracy"])
model.summary()
else:
input_img = Input(shape=img_size)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', use_bias=False)(input_img)
model = BatchNormalization()(model)
model_0 = Activation('relu')(model)
total_conv = 22 # should be even number
total_conv -= 2 # subtract first and last
residual_block_num = 5 # should be even number
for _ in range(residual_block_num): # residual block
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', use_bias=False)(model_0)
model = BatchNormalization()(model)
model = Activation('relu')(model)
print(_)
for _ in range(int(total_conv/residual_block_num)-1):
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', use_bias=False)(model)
model = BatchNormalization()(model)
model = Activation('relu')(model)
model_0 = add([model, model_0])
print(_)
model = Conv2DTranspose(64, (5, 5), padding='valid', kernel_initializer='he_normal', use_bias=False)(model)
model = BatchNormalization()(model)
model = LeakyReLU()(model)
model = Conv2D(1, (5, 5), padding='valid', kernel_initializer='he_normal')(model)
res_img = model
#input_img1 = crop(1,22,-22)(input_img)
#input_img1 = crop(2,22,-22)(input_img1)
print(input_img.shape)
output_img = merge.Add()([res_img, input_img])
# output_img = res_img
model = Model(input_img, output_img)
# model.load_weights('./vdsr_model_edges.h5')
# adam = Adam(lr=learning_rate)
adam = Adadelta()
sgd = SGD(lr=learning_rate, momentum=0.9, decay=1e-4, nesterov=False, clipnorm=1)
if optimizer == 0:
model.compile(adam, loss='mse', metrics=[ssim, ssim_metric, PSNR])
else:
model.compile(sgd, loss='mse', metrics=[ssim, ssim_metric, PSNR])
model.summary()
mycallback = MyCallback(model)
timestamp = time.strftime("%m%d-%H%M", time.localtime(time.time()))
csv_logger = callbacks.CSVLogger('data/callbacks/deconv/training_{}.log'.format(timestamp))
filepath="./checkpoints/deconv/weights-improvement-{epoch:03d}-{PSNR:.2f}-{ssim:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor=PSNR, verbose=1, mode='max')
callbacks_list = [mycallback, checkpoint, csv_logger]
with open('./model/deconv/vdsr_architecture.json', 'w') as f:
f.write(model.to_json())
history = model.fit_generator(image_gen(train_list, batch_size=batch_size),
steps_per_epoch=(409600//8)*len(train_list) // batch_size,
validation_data=image_gen(validation_list,batch_size=batch_size),
validation_steps=(409600//8)*len(validation_list) // batch_size,
epochs=epochs,
workers=1024,
callbacks=callbacks_list,
verbose=1)
print("Done training!!!")
print("Saving the final model ...")
model.save('vdsr_model.h5') # creates a HDF5 file
del model # deletes the existing model
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'validation'], loc='upper left')
# plt.show()
plt.savefig('loss.png')
plt.plot(history.history['PSNR'])
plt.plot(history.history['val_PSNR'])
plt.title('Model PSNR')
plt.ylabel('PSNR')
plt.xlabel('Epoch')
plt.legend(['Train', 'validation'], loc='upper left')
# plt.show()
plt.savefig('PSNR.png')
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(1, (3, 3), padding='same', kernel_initializer='he_normal')(model)
res_img = model
output_img = add([res_img, input_img])
model = Model(input_img, output_img)
# model.load_weights('./checkpoints/weights-improvement-20-26.93.hdf5')
adam = Adam(lr=0.00001)
sgd = SGD(lr=1e-5, momentum=0.9, decay=1e-4, nesterov=False)
model.compile(adam, loss='mse', metrics=[PSNR, "accuracy"])
model.summary()
filepath="./checkpoints/weights-improvement-{epoch:02d}-{PSNR:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor=PSNR, verbose=1, mode='max')
callbacks_list = [checkpoint]
model.fit_generator(image_gen(train_list), steps_per_epoch=len(train_list) // BATCH_SIZE, \
validation_data=image_gen(test_list), validation_steps=len(train_list) // BATCH_SIZE, \
epochs=EPOCHS, workers=8, callbacks=callbacks_list)
print("Done training!!!")
print("Saving the final model ...")
