def migrate_model(new_model):
old_model = vgg16_model(224, 224, 3)
# print(old_model.summary())
old_layers = [l for l in old_model.layers]
new_layers = [l for l in new_model.layers]
old_conv1_1 = old_model.get_layer('conv1_1')
old_weights = old_conv1_1.get_weights()[0]
old_biases = old_conv1_1.get_weights()[1]
new_weights = np.zeros((3, 3, 4, 64), dtype=np.float32)
new_weights[:, :, 0:3, :] = old_weights
new_weights[:, :, 3:4, :] = 0.0
new_conv1_1 = new_model.get_layer('conv1_1')
new_conv1_1.set_weights([new_weights, old_biases])
for i in range(2, 31):
old_layer = old_layers[i]
new_layer = new_layers[i + 1]
new_layer.set_weights(old_layer.get_weights())
# flatten = old_model.get_layer('flatten')
# f_dim = flatten.input_shape
# print('f_dim: ' + str(f_dim))
# old_dense1 = old_model.get_layer('dense1')
# input_shape = old_dense1.input_shape
# output_dim = old_dense1.get_weights()[1].shape[0]
# print('output_dim: ' + str(output_dim))
# W, b = old_dense1.get_weights()
# shape = (7, 7, 512, output_dim)
# new_W = W.reshape(shape)
# new_conv6 = new_model.get_layer('conv6')
# new_conv6.set_weights([new_W, b])
del old_model
def simnet_unit():
vgg = vgg16_model(original_shape[0], original_shape[1], channels,
num_classes)
ds = downsample_cnn_model(scaled_shape1, scaled_shape2, channels)
x = Concatenate()([vgg.output, ds.output])
# linear embedding
x = Dense(4096)(x)
inputs = [i for i in ds.inputs]
inputs.append(vgg.input)
return Model(inputs=ds.inputs + [vgg.input], outputs=x)
def migrate_model(new_model):
old_model = vgg16_model(224, 224, 3)
# print(old_model.summary())
old_layers = [l for l in old_model.layers]
new_layers = [l for l in new_model.layers]
old_conv1_1 = old_model.get_layer('conv1_1')
old_weights = old_conv1_1.get_weights()[0]
old_biases = old_conv1_1.get_weights()[1]
new_weights = np.zeros((3, 3, 3, 64), dtype=np.float32)
new_weights[:, :, 0:3, :] = old_weights
new_conv1_1 = new_model.get_layer('conv1_1')
new_conv1_1.set_weights([new_weights, old_biases])
for i in range(2, 31):
old_layer = old_layers[i]
new_layer = new_layers[i + 1]
new_layer.set_weights(old_layer.get_weights())
del old_model
def test_vgg():
X_train, X_valid, Y_train, Y_valid = split_data(X, y)
img_rows, img_cols, channels = X_train[0, :, :, :].shape
model = vgg16_model(img_rows, img_cols, channels, num_classes)
model.fit(
X_train,
Y_train,
batch_size=batch_size,
epochs=nb_epoch,
shuffle=True,
verbose=1,
validation_data=(X_valid, Y_valid),
)
predictions_valid = model.predict(X_valid,
batch_size=batch_size,
verbose=1)
score = log_loss(Y_valid, predictions_valid)
print(score)
def migrate_model(new_model):
vgg_16 = [4,7,9,12,14,16,19,21,26,28,30]
v_16 = [2,4,5,7,8,9,11,12,24,25,26]
old_model = vgg16_model(224, 224, 3)
# print(old_model.summary())
old_layers = [l for l in old_model.layers]
new_layers = [l for l in new_model.layers]
old_conv1_1 = old_model.get_layer('conv1_1')
old_weights = old_conv1_1.get_weights()[0]
old_biases = old_conv1_1.get_weights()[1]
new_weights = np.zeros((3, 3, channel, 64), dtype=np.float32)
new_weights[:, :, 0:3, :] = old_weights
new_weights[:, :, 3:channel, :] = 0.0
new_conv1_1 = new_model.get_layer('conv1_1')
new_conv1_1.set_weights([new_weights, old_biases])
for i in range(11):
index_vgg = vgg_16[i]
index_model = v_16[i]
old_layer = old_layers[index_vgg-1]
new_layer = new_layers[index_model]
new_layer.set_weights(old_layer.get_weights())
del old_model
def getVGG16(self, train_images, train_labels, load_saved_model,
model_save_path, use_pretraining, pretrained_weights_path, train_dir,
val_dir, fine_tuning_method, batch_size, num_epochs, optimizer, loss, initial_epoch, sample, lr=None):
"""
:param load_saved_model: boolean (whether to just load the model from weights path)
:param model_save_path: (final model weights path, if load_pretrained is true)
:param pretrained_weights_path: if load_trained is false and if use_pretraining is true, the path of weights to load for pre-training
:param train_dir: training data directory
:param val_dir: validation data directory
:param use_pretraining: boolean, whether to use pre-training or train from scratch
:param fine_tuning_method: whether to use end-to-end pre-training or phase-by-phase pre-training
:param batch_size: batch_size to use while fitting the model
:param num_epochs: number of epochs to train the model
:param optimizer: type of optimizer to use (sgd|adagrad)
:param loss: type of loss to use (mse|l1)
:param initial_epoch: starting epoch to start training
:return: Returns the AlexNet model according to the parameters provided
"""
print(get_time_string() + 'Creating VGG16 model..')
img_rows, img_cols = 224, 224 # Resolution of inputs
channels = 3
if load_saved_model:
if model_save_path is None:
raise Exception('Unable to load trained model as model_save_path is None!')
print(get_time_string() + 'Loading saved model from ' + model_save_path + '..')
model = load_model(model_save_path)
else:
model = vgg16_model(img_rows=img_rows, img_cols=img_cols, channels=channels,
num_classes=NUM_CLASSES_YEARBOOK,
use_pretraining=use_pretraining, pretrained_weights_path=pretrained_weights_path,
optimizer=optimizer, loss=loss, fine_tuning_method=fine_tuning_method)
if initial_epoch >= num_epochs:
print(get_time_string() + 'Not fitting the model since initial_epoch is >= num_epochs. Returning model..')
return model
# Start Fine-tuning
print(get_time_string() + 'Fitting the model..')
for e in range(initial_epoch, num_epochs):
print_line()
print('Starting epoch ' + str(e))
print_line()
completed = 0
for x_chunk, y_chunk in chunks(train_images, train_labels, batch_size, VGG16_ARCHITECTURE):
print(get_time_string() + 'Fitting model for chunk of size ' + str(len(x_chunk)) + '...')
model.fit(x_chunk, y_chunk,
batch_size=batch_size,
nb_epoch=1,
verbose=1
)
completed += len(x_chunk)
print(get_time_string() + str(completed) + ' of ' + str(len(train_images)) + ' complete. ')
print(get_time_string() + 'Epoch ' + str(e) + ' complete.')
file_name = self.getCheckpointFileName(base_model_save_path=model_save_path, epoch=e)
print(get_time_string() + 'Saving model to ' + file_name)
model.save(file_name)
print(get_time_string() + 'Evaluating on validation set..')
evaluateYearbookFromModel(model=model, architecture=VGG16_ARCHITECTURE, sample=sample)
print_line()
# model.fit(processed_train_images, train_labels,
# batch_size=batch_size,
# nb_epoch=num_epochs,
# shuffle=True,
# verbose=1, validation_data=(processed_valid_images, valid_labels),
# callbacks=[self.getCheckpointer(model_save_path)],
# initial_epoch=initial_epoch
# )
print(get_time_string() + 'Fitting complete. Returning model..')
if model_save_path is not None:
print(get_time_string() + 'Saving final model to ' + model_save_path + '..')
model.save(model_save_path)
return model
num_classes = 10
# nb_epoch = 100
train_size = 50000
valid_size = 10000
INIT_LR = 1e-3
# Load Cifar10 data. Please implement your own load_data() module for your own dataset
print("[INFO] loading CIFAR-10 data...")
X_train, Y_train, X_valid, Y_valid = load_cifar10_data(img_rows, img_cols,
train_size, valid_size,
num_classes)
print("Num of GPU requested", G)
if G <= 1:
print("[INFO] training with 1 GPU...")
model = vgg16_model(img_rows, img_cols, channel, num_classes)
# otherwise, we are compiling using multiple GPUs
else:
print("[INFO] training with {} GPUs...".format(G))
# we'll store a copy of the model on *every* GPU and then combine
# the results from the gradient updates on the CPU
with tf.device("/cpu:0"):
# initialize the model
model = vgg16_model(img_rows, img_cols, channel, num_classes)
# make the model parallel
model = multi_gpu_model(model, gpus=G)
# initialize the optimizer and model
