def load_model_preact_res(img_pl, training_pl, config):
net_arch = na.Network_Architecture(
img_pl, config['kernel_size'], config['num_kernels'],
config['strides'], config['data_format'], config['padding'],
config['pool_size'], training_pl, config['dense_layer_neurons'],
config['NUM_CLASSES'], config['dropout_rate'], config['pool_size'])
net_arch.preactivation_residual_encoder()
logits = net_arch.logits
return logits
def batch_train():
print("Reading the config file..................")
config = read_config_file(param_config_file_name)
model_to_use = config['model_to_use']
print("Reading the config file completed........")
print("")
print("Initializing.............................")
model_directory = config['model_directory'][model_to_use] + str(config['num_epochs'])
init(model_directory)
print("Initializing completed...................")
print("")
print("Reading train data.......................")
all_images, all_labels = get_data(bool(config['TRAINING']), config['DATA_PATH'], config['data_format'])
print("All data shape : " + str(all_images.shape))
print("All labels shape : " + str(all_labels.shape))
print("Reading train data completed.............")
print("")
print("Preprocessing the data...................")
all_labels = get_preprocessed_labels(all_labels, bool(config['TRAINING']))
train_images, train_labels, valid_images, valid_labels = get_train_validation_set(all_images, all_labels)
print("Preprocessing of the data completed......")
print("")
print("Building the network.....................")
if config['data_format'] == 'channels_last':
IMAGE_PLACEHOLDER_SHAPE = [None] + config['TARGET_IMAGE_SIZE'] + [config['NUM_CHANNELS']]
else:
IMAGE_PLACEHOLDER_SHAPE = [None] + [config['NUM_CHANNELS']] + config['TARGET_IMAGE_SIZE']
LABEL_PLACEHOLDER_SHAPE = [None] + [config['NUM_CLASSES']]
img_pl, lbl_pl = get_placeholders(img_placeholder_shape = IMAGE_PLACEHOLDER_SHAPE, training = bool(config['TRAINING']), lbl_placeholder_shape = LABEL_PLACEHOLDER_SHAPE)
training_pl = tf.placeholder(tf.bool)
if model_to_use == 0:
net_arch = na.Network_Architecture(img_pl, config['kernel_size'], config['num_kernels'], config['strides'], config['data_format'], config['padding'], config['pool_size'], training_pl, config['dense_layer_neurons'], config['NUM_CLASSES'], config['dropout_rate'])
net_arch.vgg_encoder()
logits = net_arch.logits
elif model_to_use == 1:
net_arch = na.Network_Architecture(img_pl, config['kernel_size'], config['num_kernels'], config['strides'], config['data_format'], config['padding'], config['pool_size'], training_pl, config['dense_layer_neurons'], config['NUM_CLASSES'], config['dropout_rate'], config['reduction_strides'])
net_arch.residual_encoder()
logits = net_arch.logits
else:
net_arch = na.Network_Architecture(img_pl, config['kernel_size'], config['num_kernels'], config['strides'], config['data_format'], config['padding'], config['pool_size'], training_pl, config['dense_layer_neurons'], config['NUM_CLASSES'], config['dropout_rate'], config['reduction_strides'])
net_arch.preactivation_residual_encoder()
logits = net_arch.logits
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
loss = get_loss_function(lbl_pl, logits)
optimizer = get_optimizer(config['learning_rate'], loss)
print("Building the network completed...........")
print("")
num_epochs = config['num_epochs']
batch_size = config['batch_size']
num_batches = int(math.ceil(train_images.shape[0] / float(batch_size)))
print("Train Images shape : " + str(train_images.shape))
print("Train Labels shape : " + str(train_labels.shape))
print("Validation Images shape : " + str(valid_images.shape))
print("Validation Labels shape : " + str(valid_labels.shape))
print("Number of epochs to train : " + str(num_epochs))
print("Batch size : " + str(batch_size))
print("Number of batches : " + str(num_batches))
print("")
print("Training the Network.....................")
ss = tf.Session()
ss.run(tf.global_variables_initializer())
train_loss_per_epoch = list()
valid_loss_per_epoch = list()
for epoch in range(num_epochs):
ti = time.time()
temp_loss_per_epoch = 0
train_images, train_labels = shuffle(train_images, train_labels)
for batch_id in range(num_batches):
batch_images = train_images[batch_id * batch_size : (batch_id + 1) * batch_size]
batch_labels = train_labels[batch_id * batch_size : (batch_id + 1) * batch_size]
_, _, loss_per_batch = ss.run([optimizer, extra_update_ops, loss], feed_dict = {img_pl : batch_images, lbl_pl : batch_labels, training_pl : bool(config['TRAINING'])})
temp_loss_per_epoch += (batch_labels.shape[0] * loss_per_batch)
ti = time.time() - ti
loss_validation_set = ss.run(loss, feed_dict = {img_pl : valid_images, lbl_pl : valid_labels, training_pl : not(config['TRAINING'])})
train_loss_per_epoch.append(temp_loss_per_epoch)
valid_loss_per_epoch.append(loss_validation_set)
print("Epoch : " + str(epoch+1) + "/" + str(num_epochs) + ", time taken : " + str(ti) + " sec.")
print("Avg. training loss : " + str(temp_loss_per_epoch / train_images.shape[0]))
print("Avg. validation loss : " + str(loss_validation_set))
print("")
if (epoch + 1) % 10 == 0:
save_model(ss, model_directory, config['model_file'][model_to_use], epoch)
print("Training the Network Completed...........")
print("")
print("Saving the model.........................")
save_model(ss, model_directory, config['model_file'][model_to_use], epoch)
train_loss_per_epoch = np.array(train_loss_per_epoch)
valid_loss_per_epoch = np.array(valid_loss_per_epoch)
train_loss_per_epoch = np.true_divide(train_loss_per_epoch, train_images.shape[0])
losses_dict = dict()
losses_dict['train_loss'] = train_loss_per_epoch
losses_dict['valid_loss'] = valid_loss_per_epoch
np.save(os.path.join(os.getcwd(), os.path.join(model_directory, config['model_metrics'][model_to_use])), (losses_dict))
print("Saving the model Completed...............")
print("")
ss.close()
