def modelChoice(args):
print("creating model..." + args.model)
if args.model == 'googleNet':
model = googleNet.build(width=args.width,
height=args.height,
depth=3,
classes=CLASS_NUM)
return model
elif args.model == 'AlexNet':
model = AlexNet.build(width=args.width,
height=args.height,
depth=3,
classes=CLASS_NUM)
return model
elif args.model == 'VGG16':
model = VGG16.build(width=args.width,
height=args.height,
depth=3,
classes=CLASS_NUM)
return model
else:
model = LeNet.build(width=args.width,
height=args.height,
depth=3,
classes=CLASS_NUM)
return model
def train(aug,trainX,trainY,testX,testY,args):
# initialize the model
print("[INFO] compiling model...")
model = LeNet.build(width=norm_size, height=norm_size, depth=3, classes=CLASS_NUM)
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="categorical_crossentropy", optimizer=opt,
metrics=["accuracy"])
# train the network
print("[INFO] training network...")
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=BS),
validation_data=(testX, testY), steps_per_epoch=len(trainX) // BS,
epochs=EPOCHS, verbose=1)
# save the model to disk
print("[INFO] serializing network...")
model.save(args["model"])
# plot the training loss and accuracy
plt.style.use("ggplot")
plt.figure()
N = EPOCHS
plt.plot(np.arange(0, N), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, N), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy on Invoice classifier")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="lower left")
plt.savefig(args["plot"])
def train(aug,trainX,trainY,testX,testY,args):
# initialize the model
print("[INFO] compiling model...")
model = LeNet.build(width=norm_size, height=norm_size, depth=3, classes=CLASS_NUM)
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="categorical_crossentropy", optimizer=opt,
metrics=["accuracy"])
# train the network
print("[INFO] training network...")
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=BS),
validation_data=(testX, testY), steps_per_epoch=len(trainX) // BS,
epochs=EPOCHS, verbose=1)
# save the model to disk
print("[INFO] serializing network...")
model.save(args["model"])
# plot the training loss and accuracy
plt.style.use("ggplot")
plt.figure()
N = EPOCHS
plt.plot(np.arange(0, N), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, N), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy on traffic-sign classifier")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="lower left")
plt.savefig(args["plot"])
# USAGE # python visualize_architecture.py # import the necessary packages from net.lenet import LeNet from keras.utils import plot_model # initialize LeNet and then write the network architecture # visualization grpah to disk model = LeNet.build(28, 28, 1, 10) plot_model(model, to_file="lenet.png", show_shapes=True)
model_zoo = [
'lenet', 'alexnet', 'vgg11', 'vgg16', 'vgg_imageNet', 'googlenetv1',
'resnet', 'densenet'
]
model_to_train = ['densenet']
for op in op_to_run:
if op_to_run == optimizer_zoo[0]:
optimizer = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
else:
optimizer = SGD(lr=INIT_LR)
for model_name in model_to_train:
if model_name == model_zoo[0]: # LeNet
model = LeNet.build(input_shape, CLASS_NUM)
elif model_name == model_zoo[1]: # AlexNet
model = AlexNet.build(input_shape,
CLASS_NUM,
dense_layers=2,
hidden_units=512,
dropout_rate=0.5,
subsample_initial_block=False)
elif model_name == model_zoo[2]: # VGG11
model = Vgg11.build(input_shape,
CLASS_NUM,
dense_layers=2,
hidden_units=512,
dropout_rate=0.5,
sc_ratio=4)
elif model_name == model_zoo[3]: # VGG16
# TF placeholder for graph input and output
x = tf.placeholder(tf.float32, [FLAGS.batch_size, 227, 227, 3])
y = tf.placeholder(tf.float32, [FLAGS.batch_size, NUM_CLASSES])
# x = val_data.images
# y = val_data.labels
keep_prob = tf.placeholder(tf.float32)
# Initialize model
if FLAGS.network == 'AlexNet':
model = AlexNet(x, keep_prob, NUM_CLASSES, False)
elif FLAGS.network == 'Vgg16':
model = Vgg16(x, keep_prob, NUM_CLASSES, False)
elif FLAGS.network == 'Vgg19':
model = Vgg19(x, keep_prob, NUM_CLASSES, False)
elif FLAGS.network == 'LeNet':
model = LeNet(x, keep_prob, NUM_CLASSES, False)
elif FLAGS.network == 'CifarNet':
model = CifarNet(x, keep_prob, NUM_CLASSES, False)
else:
print("Model not supported")
exit()
# Link variable to model output
score = tf.nn.softmax(model.logits)
# Evaluation op: Accuracy of the model
with tf.name_scope("accuracy"):
correct_pred = tf.equal(tf.argmax(score, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
correct_pred_5 = tf.nn.in_top_k(predictions=score,
targets=tf.argmax(y, 1),
input_shape = (img_rows, img_cols, 1)
chanDim = -1
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = LeNet.build(chanDim, input_shape, num_classes)
#model.compile(loss='mse', optimizer=SGD(lr=0.1), metrics=['accuracy'])
#model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.1), metrics=['accuracy'])
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
#start train-----------------------------------
#model.fit(x_train, y_train, batch_size=1000, epochs=20)
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
])
# 构建MyDataset实例
train_data = RMBDataset(data_dir=train_dir, transform=train_transform)
valid_data = RMBDataset(data_dir=valid_dir, transform=valid_transform)
# 构建DataLoder
train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE)
# ============================ step 2/5 模型 ============================
net = LeNet(classes=2)
net.initialize_weights()
# ============================ step 3/5 损失函数 ============================
criterion = nn.CrossEntropyLoss() # 选择损失函数
# ============================ step 4/5 优化器 ============================
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9) # 选择优化器
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1) # 设置学习率下降策略
# ============================ step 5/5 训练 ============================
train_curve = list()
valid_curve = list()
for epoch in range(MAX_EPOCH):
def main(_):
if FLAGS.dataset == 'flowers':
training_filenames = glob.glob('/data2/zli/flowers/*train-*-*')
validation_filenames = glob.glob('/data2/zli/flowers/*validation-*-*')
ImageDataProvider = cifar_data_provider.ImageDataProvider
NUM_CLASSES = 5
INPUT_SHAPE = [FLAGS.batch_size, 32, 32, 3]
TRAIN_SIZE = 3320
VAL_SIZE = 350
elif FLAGS.dataset == 'mnist':
training_filenames = glob.glob('/data2/zli/mnist/*train*')
validation_filenames = glob.glob('/data2/zli/mnist/*test*')
ImageDataProvider = mnist_data_provider.ImageDataProvider
NUM_CLASSES = 10
INPUT_SHAPE = [FLAGS.batch_size, 28, 28, 1]
TRAIN_SIZE = 60000
VAL_SIZE = 10000
elif FLAGS.dataset == 'cifar10':
training_filenames = glob.glob('/data2/zli/cifar10/*train*')
validation_filenames = glob.glob('/data2/zli/cifar10/*test*')
ImageDataProvider = cifar_data_provider.ImageDataProvider
NUM_CLASSES = 10
INPUT_SHAPE = [FLAGS.batch_size, 32, 32, 3]
TRAIN_SIZE = 50000
VAL_SIZE = 10000
elif FLAGS.dataset == 'imagenet':
training_filenames = glob.glob('/data2/siyu/train-*-*')
validation_filenames = glob.glob('/data2/siyu/validation-*-*')
ImageDataProvider = data_provider.ImageDataProvider
NUM_CLASSES = 1000
INPUT_SHAPE = [FLAGS.batch_size, 227, 227, 3]
TRAIN_SIZE = 1281167
VAL_SIZE = 50000
else:
print("Dataset not supported")
exit()
# Create parent path if it doesn't exist
if not os.path.isdir(FLAGS.train_dir):
os.makedirs(FLAGS.train_dir)
with tf.Graph().as_default():
global_step = tf.train.get_or_create_global_step()
with tf.device('/cpu:0'):
# Place data loading and preprocessing on the cpu
tr_data = ImageDataProvider(training_filenames,
mode='training',
batch_size=FLAGS.batch_size,
num_classes=NUM_CLASSES,
shuffle=True)
val_data = ImageDataProvider(validation_filenames,
mode='inference',
batch_size=FLAGS.batch_size,
num_classes=NUM_CLASSES,
shuffle=False)
x = tf.placeholder(tf.float32, INPUT_SHAPE)
y = tf.placeholder(tf.float32, [FLAGS.batch_size, NUM_CLASSES])
keep_prob = tf.placeholder(tf.float32)
model = None
# Initialize model
if FLAGS.network == 'AlexNet':
model = AlexNet(x, keep_prob, NUM_CLASSES, True)
elif FLAGS.network == 'Vgg16':
model = Vgg16(x, keep_prob, NUM_CLASSES, True)
elif FLAGS.network == 'Vgg19':
model = Vgg19(x, keep_prob, NUM_CLASSES, True)
elif FLAGS.network == 'LeNet':
model = LeNet(x, keep_prob, NUM_CLASSES, True)
elif FLAGS.network == 'CifarNet':
model = CifarNet(x, keep_prob, NUM_CLASSES, True)
else:
print("Model not supported")
exit()
# Link variable to model output
score = model.logits
# List of trainable variables of the layers we want to train
# var_list = [v for v in tf.trainable_variables() if v.name.split('/')[0] in train_layers]
var_list = [v for v in tf.trainable_variables()]
if FLAGS.use_admm:
# admm update
# Iterate all the weights.
count = 0
for v in tf.trainable_variables():
if 'weights' in v.name:
if 'conv' in v.name and 'conv1' not in v.name:
u = tf.get_variable('U_{}'.format(count),
shape=v.get_shape().as_list(),
trainable=False,
initializer=tf.initializers.zeros)
z = tf.get_variable('Z_{}'.format(count),
shape=v.get_shape().as_list(),
trainable=False)
weight_shape, indices = block_circulant.generate_shape_and_indices(
v.get_shape().as_list()[2],
v.get_shape().as_list()[3],
kernel_h=v.get_shape().as_list()[0],
kernel_w=v.get_shape().as_list()[1],
block_size=FLAGS.block_size)
make_bc = tf.py_func(
block_circulant.make_block_circulant,
[v + u, weight_shape, indices], tf.float32)
make_bc_ref = tf.py_func(
block_circulant.make_block_circulant,
[v, weight_shape, indices], tf.float32)
assign_z_op = z.assign(make_bc)
assign_u_op = u.assign(u + v - z)
assign_v_op = v.assign(make_bc_ref)
tf.add_to_collection('ASSIGN_U_OP', assign_u_op)
tf.add_to_collection('ASSIGN_Z_OP', assign_z_op)
tf.add_to_collection('ASSIGN_V_OP', assign_v_op)
tf.add_to_collection('MSE', tf.nn.l2_loss(u + v - z))
count += 1
with tf.name_scope('admm_update'):
update_ops_u = tf.get_collection('ASSIGN_U_OP')
update_ops_z = tf.get_collection('ASSIGN_Z_OP')
update_ops_v = tf.get_collection('ASSIGN_V_OP')
l2_losses = tf.get_collection('MSE')
# Specify the loss function:
with tf.name_scope("cross_ent"):
cel = tf.losses.softmax_cross_entropy(onehot_labels=y,
logits=score,
label_smoothing=0.0,
weights=1.0)
if FLAGS.use_admm:
l2_loss = tf.reduce_mean(l2_losses)
total_loss = cel + FLAGS.admm_learning_rate * l2_loss
slim.summaries.add_scalar_summary(total_loss, 'Total_Loss',
'losses')
slim.summaries.add_scalar_summary(l2_loss, 'MSE_Loss',
'losses')
slim.summaries.add_scalar_summary(cel, 'CEL_Loss', 'losses')
else:
total_loss = cel
slim.summaries.add_scalar_summary(total_loss, 'Total_Loss',
'losses')
# Train op
with tf.name_scope("train"):
# Get gradients of all trainable variables
# gradients = tf.gradients(loss, var_list)
# gradients = list(zip(gradients, var_list))
# Create optimizer and apply gradient descent to the trainable variables
opt = tf.train.AdamOptimizer(FLAGS.learning_rate)
train_tensor = slim.learning.create_train_op(
total_loss,
optimizer=opt,
update_ops=tf.get_collection(tf.GraphKeys.UPDATE_OPS))
if FLAGS.retrain and FLAGS.use_admm:
grads_and_vars = opt.compute_gradients(cel)
new_grads_and_vars = []
for grad_and_var in grads_and_vars:
grad, v = grad_and_var
if len(v.get_shape().as_list()) == 4:
weight_shape, indices = block_circulant.generate_shape_and_indices(
v.get_shape().as_list()[2],
v.get_shape().as_list()[3],
kernel_h=v.get_shape().as_list()[0],
kernel_w=v.get_shape().as_list()[1],
block_size=FLAGS.block_size)
new_grad = tf.py_func(
block_circulant.make_block_circulant,
[grad, weight_shape, indices], tf.float32)
else:
new_grad = grad
new_grads_and_vars.append((new_grad, v))
grad_updates = opt.apply_gradients(new_grads_and_vars)
retrain_tensor = control_flow_ops.with_dependencies(
[grad_updates], cel)
# Summaries:
slim.summaries.add_histogram_summaries(slim.get_model_variables())
slim.summaries.add_scalar_summary(FLAGS.learning_rate, 'Learning_Rate',
'training')
train_op = {}
if FLAGS.use_admm:
if FLAGS.retrain:
train_op['update_v'] = update_ops_v
train_op['loss'] = retrain_tensor
else:
train_op['loss'] = train_tensor
train_op['Z'] = update_ops_z
train_op['U'] = update_ops_u
else:
train_op['loss'] = train_tensor
# Add the variables we train to the summary
for var in var_list:
tf.summary.histogram(var.name, var)
# Evaluation op: Accuracy of the model
with tf.name_scope("accuracy"):
correct_pred = tf.equal(tf.argmax(score, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
correct_pred_5 = tf.nn.in_top_k(predictions=score,
targets=tf.argmax(y, 1),
k=5)
top_5_accuracy = tf.reduce_mean(tf.cast(correct_pred_5,
tf.float32))
# Add the accuracy to the summary
tf.summary.scalar('accuracy', accuracy)
tf.summary.scalar('Recall@5', top_5_accuracy)
# Merge all summaries together
merged_summary = tf.summary.merge_all()
# Initialize the FileWriter
writer = tf.summary.FileWriter(FLAGS.train_dir)
# Initialize an saver for store model checkpoints
saver = tf.train.Saver(var_list=var_list, max_to_keep=100)
# Get the number of training/validation steps per epoch
train_batches_per_epoch = int(np.floor(TRAIN_SIZE / FLAGS.batch_size))
val_batches_per_epoch = int(np.floor(VAL_SIZE / FLAGS.batch_size))
# Start Tensorflow session
with tf.Session() as sess:
# Initialize all variables
sess.run(tf.global_variables_initializer())
# Add the model graph to TensorBoard
writer.add_graph(sess.graph)
if FLAGS.restore:
# Load the pretrained weights
saver.restore(sess, FLAGS.checkpoint_path)
print("{} Start training...".format(datetime.now()))
print("{} Open Tensorboard at --logdir {}".format(
datetime.now(), FLAGS.train_dir))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
print("{} Dataset initialized.".format(datetime.now()))
if FLAGS.use_admm:
if FLAGS.retrain:
_ = sess.run(train_op['update_v'])
else:
_ = sess.run(train_op['Z'])
# Loop over number of epochs
for epoch in range(FLAGS.num_epochs):
print("{} Epoch number: {}".format(datetime.now(), epoch + 1))
for step in range(train_batches_per_epoch * epoch,
train_batches_per_epoch * (epoch + 1)):
# get next batch of data
start_time = time.clock()
img_batch, label_batch = sess.run(
[tr_data.images, tr_data.labels])
elapsed_time = time.clock() - start_time
# And run the training op
start_time = time.clock()
loss_value = sess.run(train_op['loss'],
feed_dict={
x: img_batch,
y: label_batch,
keep_prob: FLAGS.dropout_rate
})
# display loss
if step % display_step == 0:
elapsed_time = time.clock() - start_time
print('step:{} ({} s/step), loss = {:4f}'.format(
step, elapsed_time / float(display_step),
loss_value))
if FLAGS.use_admm:
# admm_update
if not FLAGS.retrain:
if (step + 1) % admm_update_step == 0:
start_time = time.clock()
_ = sess.run(train_op['Z'])
_ = sess.run(train_op['U'])
elapsed_time = time.clock() - start_time
print(
'admm step:{} s/step'.format(elapsed_time))
# Generate summary with the current batch of data and write to file
if (step + 1) % summary_step == 0:
s = sess.run(merged_summary,
feed_dict={
x: img_batch,
y: label_batch,
keep_prob: 1.
})
writer.add_summary(
s, epoch * train_batches_per_epoch + step)
# Validate the model on the entire validation set
print("{} Start validation".format(datetime.now()))
test_acc = 0.
test_acc_5 = 0.
test_count = 0
start = time.clock()
for bc in range(val_batches_per_epoch):
img_batch, label_batch = sess.run(
[val_data.images, val_data.labels])
acc, acc_5 = sess.run([accuracy, top_5_accuracy],
feed_dict={
x: img_batch,
y: label_batch,
keep_prob: 1.
})
print("Batch {}/{}, acc {:4f}, recall@5 {:4f}".format(
bc + 1, val_batches_per_epoch, acc, acc_5))
test_acc += acc
test_acc_5 += acc_5
test_count += 1
test_acc /= test_count
test_acc_5 /= test_count
print(
"{} Validation Accuracy = {:.4f} Validation Recall@5 = {:.4f}"
.format(datetime.now(), test_acc, test_acc_5))
print("{} Saving checkpoint of model...".format(
datetime.now()))
# save checkpoint of the model
checkpoint_name = os.path.join(
FLAGS.train_dir, 'model_epoch' + str(epoch + 1) + '.ckpt')
save_path = saver.save(sess, checkpoint_name)
print("{} Model checkpoint saved at {}".format(
datetime.now(), checkpoint_name))
coord.request_stop()
coord.join(threads)
sess.close()
# partition the data into training and testing splits using 75% of
# the data for training and the remaining 25% for testing
(trainX, testX, trainY, testY) = train_test_split(data,
labels,
test_size=0.25,
random_state=42)
# convert the labels from integers to vectors
lb = LabelBinarizer().fit(trainY)
trainY = lb.transform(trainY)
testY = lb.transform(testY)
# initialize the model
print("[INFO] compiling model...")
model = LeNet.build(width=28, height=28, depth=1, classes=9)
opt = SGD(lr=0.01)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# train the network
print("[INFO] training network...")
H = model.fit(trainX,
trainY,
validation_data=(testX, testY),
batch_size=32,
epochs=15,
verbose=1)
# evaluate the network
