X_train, y_train, X_val, y_val, X_test, y_test = \

tl.files.load_mnist_dataset(shape=(-1,784))

X_train = np.asarray(X_train, dtype=np.float32)

y_train = np.asarray(y_train, dtype=np.int32)

X_val = np.asarray(X_val, dtype=np.float32)

y_val = np.asarray(y_val, dtype=np.int32)

X_test = np.asarray(X_test, dtype=np.float32)

y_test = np.asarray(y_test, dtype=np.int32)

print('X_train.shape', X_train.shape)

print('y_train.shape', y_train.shape)

print('X_val.shape', X_val.shape)

print('y_val.shape', y_val.shape)

print('X_test.shape', X_test.shape)

print('y_test.shape', y_test.shape)

print('X %s y %s' % (X_test.dtype, y_test.dtype))

sess = tf.InteractiveSession()

# placeholder

x = tf.placeholder(tf.float32, shape=[None, 784], name='x')

y_ = tf.placeholder(tf.int32, shape=[None, ], name='y_')

# Note: the softmax is implemented internally in tl.cost.cross_entropy(y, y_)

# to speed up computation, so we use identity in the last layer.

# see tf.nn.sparse_softmax_cross_entropy_with_logits()

if model == 'relu':

network = tl.layers.InputLayer(x, name='input_layer')

network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')

network = tl.layers.DenseLayer(network, n_units=800,

act = tf.nn.relu, name='relu1')

network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')

network = tl.layers.DenseLayer(network, n_units=800,

act = tf.nn.relu, name='relu2')

network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3')

network = tl.layers.DenseLayer(network, n_units=10,

act = tf.identity,

name='output_layer')

elif model == 'dropconnect':

network = tl.layers.InputLayer(x, name='input_layer')

network = tl.layers.DropconnectDenseLayer(network, keep = 0.8,

n_units=800, act = tf.nn.relu,

name='dropconnect_relu1')

network = tl.layers.DropconnectDenseLayer(network, keep = 0.5,

n_units=800, act = tf.nn.relu,

name='dropconnect_relu2')

network = tl.layers.DropconnectDenseLayer(network, keep = 0.5,

n_units=10,

act = tf.identity,

name='output_layer')

# To print all attributes of a Layer.

# attrs = vars(network)

# print(', '.join("%s: %s\n" % item for item in attrs.items()))

#

# print(network.all_drop) # {'drop1': 0.8, 'drop2': 0.5, 'drop3': 0.5}

# print(drop1, drop2, drop3) # Tensor("Placeholder_2:0", dtype=float32) Tensor("Placeholder_3:0", dtype=float32) Tensor("Placeholder_4:0", dtype=float32)

# exit()

y = network.outputs

y_op = tf.argmax(tf.nn.softmax(y), 1)

cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(y, y_))

# Alternatively, you can use TensorLayer's function to compute cost:

# cost = tl.cost.cross_entropy(y, y_)

# You can add more penalty to the cost function as follow.

# cost = cost + tl.cost.maxnorm_regularizer(1.0)(network.all_params[0]) + tl.cost.maxnorm_regularizer(1.0)(network.all_params[2])

# cost = cost + tl.cost.lo_regularizer(0.0001)(network.all_params[0]) + tl.cost.lo_regularizer(0.0001)(network.all_params[2])

# cost = cost + tl.cost.maxnorm_o_regularizer(0.001)(network.all_params[0]) + tl.cost.maxnorm_o_regularizer(0.001)(network.all_params[2])

params = network.all_params

# train

n_epoch = 100

batch_size = 128

learning_rate = 0.0001

print_freq = 5

train_op = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999,

epsilon=1e-08, use_locking=False).minimize(cost)

sess.run(tf.initialize_all_variables()) # initialize all variables

network.print_params()

network.print_layers()

print(' learning_rate: %f' % learning_rate)

print(' batch_size: %d' % batch_size)

for epoch in range(n_epoch):

start_time = time.time()

for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train,

batch_size, shuffle=True):

feed_dict = {x: X_train_a, y_: y_train_a}

feed_dict.update( network.all_drop ) # enable dropout or dropconnect layers

sess.run(train_op, feed_dict=feed_dict)

# The optional feed_dict argument allows the caller to override the value of tensors in the graph. Each key in feed_dict can be one of the following types:

# If the key is a Tensor, the value may be a Python scalar, string, list, or numpy ndarray that can be converted to the same dtype as that tensor. Additionally, if the key is a placeholder, the shape of the value will be checked for compatibility with the placeholder.

# If the key is a SparseTensor, the value should be a SparseTensorValue.

if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:

print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))

dp_dict = tl.utils.dict_to_one( network.all_drop ) # disable all dropout/dropconnect/denoising layers

feed_dict = {x: X_train, y_: y_train}

feed_dict.update(dp_dict)

print(" train loss: %f" % sess.run(cost, feed_dict=feed_dict))

dp_dict = tl.utils.dict_to_one( network.all_drop )

feed_dict = {x: X_val, y_: y_val}

feed_dict.update(dp_dict)

print(" val loss: %f" % sess.run(cost, feed_dict=feed_dict))

print(" val acc: %f" % np.mean(y_val ==

sess.run(y_op, feed_dict=feed_dict)))

try:

# You can visualize the weight of 1st hidden layer as follow.

tl.visualize.W(network.all_params[0].eval(), second=10,

saveable=True, shape=[28, 28],

name='w1_'+str(epoch+1), fig_idx=2012)

# You can also save the weight of 1st hidden layer to .npz file.

# tl.files.save_npz([network.all_params[0]] , name='w1'+str(epoch+1)+'.npz')

except:

raise Exception("You should change visualize_W(), if you want \

to save the feature images for different dataset")

print('Evaluation')

dp_dict = tl.utils.dict_to_one( network.all_drop )

feed_dict = {x: X_test, y_: y_test}

feed_dict.update(dp_dict)

print(" test loss: %f" % sess.run(cost, feed_dict=feed_dict))

print(" test acc: %f" % np.mean(y_test == sess.run(y_op,

feed_dict=feed_dict)))

# Add ops to save and restore all the variables, including variables for training.

# ref: https://www.tensorflow.org/versions/r0.8/how_tos/variables/index.html

saver = tf.train.Saver()

save_path = saver.save(sess, "model.ckpt")

print("Model saved in file: %s" % save_path)

# You can also save the parameters into .npz file.

tl.files.save_npz(network.all_params , name='model.npz')

# You can only save one parameter as follow.

# tl.files.save_npz([network.all_params[0]] , name='model.npz')

# Then, restore the parameters as follow.

# load_params = tl.files.load_npz(path='', name='model.npz')

# tl.files.assign_params(sess, load_params, network)

# In the end, close TensorFlow session.

X_train, y_train, X_val, y_val, X_test, y_test = \

tl.files.load_mnist_dataset(shape=(-1,784))

X_train = np.asarray(X_train, dtype=np.float32)

y_train = np.asarray(y_train, dtype=np.int64)

X_val = np.asarray(X_val, dtype=np.float32)

y_val = np.asarray(y_val, dtype=np.int64)

X_test = np.asarray(X_test, dtype=np.float32)

y_test = np.asarray(y_test, dtype=np.int64)

print('X_train.shape', X_train.shape)

print('y_train.shape', y_train.shape)

print('X_val.shape', X_val.shape)

print('y_val.shape', y_val.shape)

print('X_test.shape', X_test.shape)

print('y_test.shape', y_test.shape)

print('X %s y %s' % (X_test.dtype, y_test.dtype))

sess = tf.InteractiveSession()

# placeholder

x = tf.placeholder(tf.float32, shape=[None, 784], name='x')

y_ = tf.placeholder(tf.int64, shape=[None, ], name='y_')

print("Build Network")

if model == 'relu':

network = tl.layers.InputLayer(x, name='input_layer')

network = tl.layers.DropoutLayer(network, keep=0.5, name='denoising1') # if drop some inputs, it is denoise AE

network = tl.layers.DenseLayer(network, n_units=196,

act = tf.nn.relu, name='relu1')

recon_layer1 = tl.layers.ReconLayer(network, x_recon=x, n_units=784,

act = tf.nn.softplus, name='recon_layer1')

elif model == 'sigmoid':

# sigmoid - set keep to 1.0, if you want a vanilla Autoencoder

network = tl.layers.InputLayer(x, name='input_layer')

network = tl.layers.DropoutLayer(network, keep=0.5, name='denoising1')

network = tl.layers.DenseLayer(network, n_units=196,

act=tf.nn.sigmoid, name='sigmoid1')

recon_layer1 = tl.layers.ReconLayer(network, x_recon=x, n_units=784,

act=tf.nn.sigmoid, name='recon_layer1')

## ready to train

sess.run(tf.initialize_all_variables())

## print all params

print("All Network Params")

network.print_params()

## pretrain

print("Pre-train Layer 1")

recon_layer1.pretrain(sess, x=x, X_train=X_train, X_val=X_val,

denoise_name='denoising1', n_epoch=200,

batch_size=128, print_freq=10, save=True,

save_name='w1pre_')

# You can also disable denoisong by setting denoise_name=None.

# recon_layer1.pretrain(sess, x=x, X_train=X_train, X_val=X_val,

# denoise_name=None, n_epoch=500, batch_size=128,

# print_freq=10, save=True, save_name='w1pre_')

# Add ops to save and restore all the variables.

# ref: https://www.tensorflow.org/versions/r0.8/how_tos/variables/index.html

saver = tf.train.Saver()

# you may want to save the model

save_path = saver.save(sess, "model.ckpt")

print("Model saved in file: %s" % save_path)

X_train, y_train, X_val, y_val, X_test, y_test = \

tl.files.load_mnist_dataset(shape=(-1,784))

X_train = np.asarray(X_train, dtype=np.float32)

y_train = np.asarray(y_train, dtype=np.int64)

X_val = np.asarray(X_val, dtype=np.float32)

y_val = np.asarray(y_val, dtype=np.int64)

X_test = np.asarray(X_test, dtype=np.float32)

y_test = np.asarray(y_test, dtype=np.int64)

print('X_train.shape', X_train.shape)

print('y_train.shape', y_train.shape)

print('X_val.shape', X_val.shape)

print('y_val.shape', y_val.shape)

print('X_test.shape', X_test.shape)

print('y_test.shape', y_test.shape)

print('X %s y %s' % (X_test.dtype, y_test.dtype))

sess = tf.InteractiveSession()

x = tf.placeholder(tf.float32, shape=[None, 784], name='x')

y_ = tf.placeholder(tf.int64, shape=[None, ], name='y_')

if model == 'relu':

act = tf.nn.relu

act_recon = tf.nn.softplus

elif model == 'sigmoid':

act = tf.nn.sigmoid

act_recon = act

# Define network

print("\nBuild Network")

network = tl.layers.InputLayer(x, name='input_layer')

# denoise layer for AE

network = tl.layers.DropoutLayer(network, keep=0.5, name='denoising1')

# 1st layer

network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')

network = tl.layers.DenseLayer(network, n_units=800, act = act, name=model+'1')

x_recon1 = network.outputs

recon_layer1 = tl.layers.ReconLayer(network, x_recon=x, n_units=784,

act = act_recon, name='recon_layer1')

# 2nd layer

network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')

network = tl.layers.DenseLayer(network, n_units=800, act = act, name=model+'2')

recon_layer2 = tl.layers.ReconLayer(network, x_recon=x_recon1, n_units=800,

act = act_recon, name='recon_layer2')

# 3rd layer

network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3')

network = tl.layers.DenseLayer(network, n_units=10,

act = tf.identity,

name='output_layer')

# Define fine-tune process

y = network.outputs

y_op = tf.argmax(tf.nn.softmax(y), 1)

ce = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(y, y_))

cost = ce

n_epoch = 200

batch_size = 128

learning_rate = 0.0001

print_freq = 10

train_params = network.all_params

# train_op = tf.train.GradientDescentOptimizer(0.5).minimize(cost)

train_op = tf.train.AdamOptimizer(learning_rate , beta1=0.9, beta2=0.999,

epsilon=1e-08, use_locking=False).minimize(cost, var_list=train_params)

# Initialize all variables including weights, biases and the variables in train_op

sess.run(tf.initialize_all_variables())

# Pre-train

print("\nAll Network Params before pre-train")

network.print_params()

print("\nPre-train Layer 1")

recon_layer1.pretrain(sess, x=x, X_train=X_train, X_val=X_val,

denoise_name='denoising1', n_epoch=200,

batch_size=128, print_freq=10, save=True,

save_name='w1pre_')

print("\nPre-train Layer 2")

recon_layer2.pretrain(sess, x=x, X_train=X_train, X_val=X_val,

denoise_name='denoising1', n_epoch=200,

batch_size=128, print_freq=10, save=False)

print("\nAll Network Params after pre-train")

network.print_params()

# Fine-tune

print("\nFine-tune Network")

correct_prediction = tf.equal(tf.argmax(y, 1), y_)

acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

print(' learning_rate: %f' % learning_rate)

print(' batch_size: %d' % batch_size)

for epoch in range(n_epoch):

start_time = time.time()

for X_train_a, y_train_a in tl.iterate.minibatches(

X_train, y_train, batch_size, shuffle=True):

feed_dict = {x: X_train_a, y_: y_train_a}

feed_dict.update( network.all_drop ) # enable noise layers

feed_dict[set_keep['denoising1']] = 1 # disable denoising layer

sess.run(train_op, feed_dict=feed_dict)

if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:

print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))

train_loss, train_acc, n_batch = 0, 0, 0

for X_train_a, y_train_a in tl.iterate.minibatches(

X_train, y_train, batch_size, shuffle=True):

dp_dict = tl.utils.dict_to_one( network.all_drop ) # disable noise layers

feed_dict = {x: X_train_a, y_: y_train_a}

feed_dict.update(dp_dict)

err, ac = sess.run([cost, acc], feed_dict=feed_dict)

train_loss += err

train_acc += ac

n_batch += 1

print(" train loss: %f" % (train_loss/ n_batch))

print(" train acc: %f" % (train_acc/ n_batch))

val_loss, val_acc, n_batch = 0, 0, 0

for X_val_a, y_val_a in tl.iterate.minibatches(

X_val, y_val, batch_size, shuffle=True):

dp_dict = tl.utils.dict_to_one( network.all_drop ) # disable noise layers

feed_dict = {x: X_val_a, y_: y_val_a}

feed_dict.update(dp_dict)

err, ac = sess.run([cost, acc], feed_dict=feed_dict)

val_loss += err

val_acc += ac

n_batch += 1

print(" val loss: %f" % (val_loss/ n_batch))

print(" val acc: %f" % (val_acc/ n_batch))

try:

# visualize the 1st hidden layer during fine-tune

tl.visualize.W(network.all_params[0].eval(), second=10,

saveable=True, shape=[28, 28],

name='w1_'+str(epoch+1), fig_idx=2012)

except:

raise Exception("# You should change visualize_W(), if you want to save the feature images for different dataset")

print('Evaluation')

test_loss, test_acc, n_batch = 0, 0, 0

for X_test_a, y_test_a in tl.iterate.minibatches(

X_test, y_test, batch_size, shuffle=True):

dp_dict = tl.utils.dict_to_one( network.all_drop ) # disable noise layers

feed_dict = {x: X_test_a, y_: y_test_a}

feed_dict.update(dp_dict)

err, ac = sess.run([cost, acc], feed_dict=feed_dict)

test_loss += err

test_acc += ac

n_batch += 1

print(" test loss: %f" % (test_loss/n_batch))

print(" test acc: %f" % (test_acc/n_batch))

# print(" test acc: %f" % np.mean(y_test == sess.run(y_op, feed_dict=feed_dict)))

# Add ops to save and restore all the variables.

# ref: https://www.tensorflow.org/versions/r0.8/how_tos/variables/index.html

saver = tf.train.Saver()

# you may want to save the model

save_path = saver.save(sess, "model.ckpt")

print("Model saved in file: %s" % save_path)

"""Reimplementation of the TensorFlow official MNIST CNN tutorials:

X_train, y_train, X_val, y_val, X_test, y_test = \

tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))

X_train = np.asarray(X_train, dtype=np.float32)

y_train = np.asarray(y_train, dtype=np.int64)

X_val = np.asarray(X_val, dtype=np.float32)

y_val = np.asarray(y_val, dtype=np.int64)

X_test = np.asarray(X_test, dtype=np.float32)

y_test = np.asarray(y_test, dtype=np.int64)

print('X_train.shape', X_train.shape)

print('y_train.shape', y_train.shape)

print('X_val.shape', X_val.shape)

print('y_val.shape', y_val.shape)

print('X_test.shape', X_test.shape)

print('y_test.shape', y_test.shape)

print('X %s y %s' % (X_test.dtype, y_test.dtype))

sess = tf.InteractiveSession()

# Define the batchsize at the begin, you can give the batchsize in x and y_

# rather than 'None', this can allow TensorFlow to apply some optimizations

# – especially for convolutional layers.

batch_size = 128

x = tf.placeholder(tf.float32, shape=[batch_size, 28, 28, 1]) # [batch_size, height, width, channels]

y_ = tf.placeholder(tf.int64, shape=[batch_size,])

network = tl.layers.InputLayer(x, name='input_layer')

network = tl.layers.Conv2dLayer(network,

act = tf.nn.relu,

shape = [5, 5, 1, 32], # 32 features for each 5x5 patch

strides=[1, 1, 1, 1],

padding='SAME',

name ='cnn_layer1') # output: (?, 28, 28, 32)

network = tl.layers.PoolLayer(network,

ksize=[1, 2, 2, 1],

strides=[1, 2, 2, 1],

padding='SAME',

pool = tf.nn.max_pool,

name ='pool_layer1',) # output: (?, 14, 14, 32)

network = tl.layers.Conv2dLayer(network,

act = tf.nn.relu,

shape = [5, 5, 32, 64], # 64 features for each 5x5 patch

strides=[1, 1, 1, 1],

padding='SAME',

name ='cnn_layer2') # output: (?, 14, 14, 64)

network = tl.layers.PoolLayer(network,

ksize=[1, 2, 2, 1],

strides=[1, 2, 2, 1],

padding='SAME',

pool = tf.nn.max_pool,

name ='pool_layer2',) # output: (?, 7, 7, 64)

network = tl.layers.FlattenLayer(network, name='flatten_layer') # output: (?, 3136)

network = tl.layers.DropoutLayer(network, keep=0.5, name='drop1') # output: (?, 3136)

network = tl.layers.DenseLayer(network, n_units=256,

act = tf.nn.relu, name='relu1') # output: (?, 256)

network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2') # output: (?, 256)

network = tl.layers.DenseLayer(network, n_units=10,

act = tf.identity,

name='output_layer') # output: (?, 10)

y = network.outputs

ce = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(y, y_))

cost = ce

correct_prediction = tf.equal(tf.argmax(y, 1), y_)

acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# train

n_epoch = 200

learning_rate = 0.0001

print_freq = 10

train_params = network.all_params

train_op = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999,

epsilon=1e-08, use_locking=False).minimize(cost, var_list=train_params)

sess.run(tf.initialize_all_variables())

network.print_params()

network.print_layers()

print(' learning_rate: %f' % learning_rate)

print(' batch_size: %d' % batch_size)

for epoch in range(n_epoch):

start_time = time.time()

for X_train_a, y_train_a in tl.iterate.minibatches(

X_train, y_train, batch_size, shuffle=True):

feed_dict = {x: X_train_a, y_: y_train_a}

feed_dict.update( network.all_drop ) # enable noise layers

sess.run(train_op, feed_dict=feed_dict)

if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:

print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))

train_loss, train_acc, n_batch = 0, 0, 0

for X_train_a, y_train_a in tl.iterate.minibatches(

X_train, y_train, batch_size, shuffle=True):

dp_dict = tl.utils.dict_to_one( network.all_drop ) # disable noise layers

feed_dict = {x: X_train_a, y_: y_train_a}

feed_dict.update(dp_dict)

err, ac = sess.run([cost, acc], feed_dict=feed_dict)

train_loss += err; train_acc += ac; n_batch += 1

print(" train loss: %f" % (train_loss/ n_batch))

print(" train acc: %f" % (train_acc/ n_batch))

val_loss, val_acc, n_batch = 0, 0, 0

for X_val_a, y_val_a in tl.iterate.minibatches(

X_val, y_val, batch_size, shuffle=True):

dp_dict = tl.utils.dict_to_one( network.all_drop ) # disable noise layers

feed_dict = {x: X_val_a, y_: y_val_a}

feed_dict.update(dp_dict)

err, ac = sess.run([cost, acc], feed_dict=feed_dict)

val_loss += err; val_acc += ac; n_batch += 1

print(" val loss: %f" % (val_loss/ n_batch))

print(" val acc: %f" % (val_acc/ n_batch))

try:

tl.visualize.CNN2d(network.all_params[0].eval(),

second=10, saveable=True,

name='cnn1_'+str(epoch+1), fig_idx=2012)

except:

raise Exception("# You should change visualize.CNN(), if you want to save the feature images for different dataset")

print('Evaluation')

test_loss, test_acc, n_batch = 0, 0, 0

for X_test_a, y_test_a in tl.iterate.minibatches(

X_test, y_test, batch_size, shuffle=True):

dp_dict = tl.utils.dict_to_one( network.all_drop ) # disable noise layers

feed_dict = {x: X_test_a, y_: y_test_a}

feed_dict.update(dp_dict)

err, ac = sess.run([cost, acc], feed_dict=feed_dict)

test_loss += err; test_acc += ac; n_batch += 1

print(" test loss: %f" % (test_loss/n_batch))