def test_mnist():
"""Train an autoencoder on MNIST.
This function will train an autoencoder on MNIST and also
save many image files during the training process, demonstrating
the latent space of the inner most dimension of the encoder,
as well as reconstructions of the decoder.
"""
# load MNIST
n_code = 2
mnist = MNIST(split=[0.8, 0.1, 0.1])
ae = VAE(input_shape=[None, 784],
n_filters=[512, 256],
n_hidden=64,
n_code=n_code,
activation=tf.nn.sigmoid,
convolutional=False,
variational=True)
n_examples = 100
zs = np.random.uniform(-1.0, 1.0, [4, n_code]).astype(np.float32)
zs = utils.make_latent_manifold(zs, n_examples)
learning_rate = 0.02
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
ae['cost'])
# We create a session to use the graph
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Fit all training data
t_i = 0
batch_i = 0
batch_size = 200
n_epochs = 10
test_xs = mnist.test.images[:n_examples]
utils.montage(test_xs.reshape((-1, 28, 28)), 'test_xs.png')
for epoch_i in range(n_epochs):
train_i = 0
train_cost = 0
for batch_xs, _ in mnist.train.next_batch(batch_size):
train_cost += sess.run([ae['cost'], optimizer],
feed_dict={
ae['x']: batch_xs,
ae['train']: True,
ae['keep_prob']: 1.0
})[0]
train_i += 1
if batch_i % 10 == 0:
# Plot example reconstructions from latent layer
recon = sess.run(ae['y'],
feed_dict={
ae['z']: zs,
ae['train']: False,
ae['keep_prob']: 1.0
})
utils.montage(recon.reshape((-1, 28, 28)),
'manifold_%08d.png' % t_i)
# Plot example reconstructions
recon = sess.run(ae['y'],
feed_dict={
ae['x']: test_xs,
ae['train']: False,
ae['keep_prob']: 1.0
})
utils.montage(recon.reshape((-1, 28, 28)),
'reconstruction_%08d.png' % t_i)
t_i += 1
batch_i += 1
valid_i = 0
valid_cost = 0
for batch_xs, _ in mnist.valid.next_batch(batch_size):
valid_cost += sess.run([ae['cost']],
feed_dict={
ae['x']: batch_xs,
ae['train']: False,
ae['keep_prob']: 1.0
})[0]
valid_i += 1
print('train:', train_cost / train_i, 'valid:', valid_cost / valid_i)
def train_vae(files,
input_shape=[None, 784],
output_shape=[None, 784],
learning_rate=0.0001,
batch_size=128,
n_epochs=50,
crop_shape=[64, 64],
crop_factor=0.8,
n_filters=[100, 100, 100, 100],
n_hidden=256,
n_code=50,
denoising=True,
convolutional=True,
variational=True,
softmax=False,
classifier='alexnet_v2',
filter_sizes=[3, 3, 3, 3],
dropout=True,
keep_prob=0.8,
activation=tf.nn.relu,
img_step=1000,
save_step=2500,
output_path="result",
ckpt_name="vae.ckpt"):
"""General purpose training of a (Variational) (Convolutional) Autoencoder.
Supply a list of file paths to images, and this will do everything else.
Parameters
----------
files : list of strings
List of paths to images.
input_shape : list
Must define what the input image's shape is.
learning_rate : float, optional
Learning rate.
batch_size : int, optional
Batch size.
n_epochs : int, optional
Number of epochs.
n_examples : int, optional
Number of example to use while demonstrating the current training
iteration's reconstruction. Creates a square montage, so make
sure int(sqrt(n_examples))**2 = n_examples, e.g. 16, 25, 36, ... 100.
crop_shape : list, optional
Size to centrally crop the image to.
crop_factor : float, optional
Resize factor to apply before cropping.
n_filters : list, optional
Same as VAE's n_filters.
n_hidden : int, optional
Same as VAE's n_hidden.
n_code : int, optional
Same as VAE's n_code.
convolutional : bool, optional
Use convolution or not.
variational : bool, optional
Use variational layer or not.
softmax : bool, optional
Use the classification network or not.
classifier : str, optional
Network for classification.
filter_sizes : list, optional
Same as VAE's filter_sizes.
dropout : bool, optional
Use dropout or not
keep_prob : float, optional
Percent of keep for dropout.
activation : function, optional
Which activation function to use.
img_step : int, optional
How often to save training images showing the manifold and
reconstruction.
save_step : int, optional
How often to save checkpoints.
ckpt_name : str, optional
Checkpoints will be named as this, e.g. 'model.ckpt'
"""
batch_train = create_input_pipeline(files=files,
batch_size=batch_size,
n_epochs=n_epochs,
crop_shape=crop_shape,
crop_factor=crop_factor,
input_shape=input_shape,
output_shape=output_shape)
if softmax:
batch_imagenet = create_input_pipeline(
files="./list_annotated_imagenet.csv",
batch_size=batch_size,
n_epochs=n_epochs,
crop_shape=crop_shape,
crop_factor=crop_factor,
input_shape=input_shape,
output_shape=output_shape)
batch_pascal = create_input_pipeline(
files="./list_annotated_pascal.csv",
batch_size=batch_size,
n_epochs=n_epochs,
crop_shape=crop_shape,
crop_factor=crop_factor,
input_shape=input_shape,
output_shape=output_shape)
batch_shapenet = create_input_pipeline(
files="./list_annotated_img_test.csv",
batch_size=batch_size,
n_epochs=n_epochs,
crop_shape=crop_shape,
crop_factor=crop_factor,
input_shape=input_shape,
output_shape=output_shape)
ae = VAE(input_shape=[None] + crop_shape + [input_shape[-1]],
output_shape=[None] + crop_shape + [output_shape[-1]],
denoising=denoising,
convolutional=convolutional,
variational=variational,
softmax=softmax,
n_filters=n_filters,
n_hidden=n_hidden,
n_code=n_code,
dropout=dropout,
filter_sizes=filter_sizes,
activation=activation,
classifier=classifier)
with open(files, "r") as f:
reader = csv.reader(f, delimiter=",")
data = list(reader)
n_files = len(data)
# Create a manifold of our inner most layer to show
# example reconstructions. This is one way to see
# what the "embedding" or "latent space" of the encoder
# is capable of encoding, though note that this is just
# a random hyperplane within the latent space, and does not
# encompass all possible embeddings.
np.random.seed(1)
zs = np.random.uniform(-1.0, 1.0, [4, n_code]).astype(np.float32)
zs = utils.make_latent_manifold(zs, 6)
optimizer_vae = tf.train.AdamOptimizer(
learning_rate=learning_rate).minimize(ae['cost_vae'])
if softmax:
# AlexNet for 0.01,
# Iception v1 for 0.01
# SqueezeNet for 0.01
if classifier == 'inception_v3':
lr = tf.train.exponential_decay(0.1,
0,
n_files / batch_size * 20,
0.16,
staircase=True)
optimizer_softmax = tf.train.RMSPropOptimizer(
lr, decay=0.9, momentum=0.9,
epsilon=0.1).minimize(ae['cost_s'])
elif classifier == 'inception_v2':
optimizer_softmax = tf.train.AdamOptimizer(
learning_rate=0.01).minimize(ae['cost_s'])
elif classifier == 'inception_v1':
optimizer_softmax = tf.train.GradientDescentOptimizer(
learning_rate=0.01).minimize(ae['cost_s'])
elif (classifier == 'squeezenet') or (classifier == 'zigzagnet'):
optimizer_softmax = tf.train.RMSPropOptimizer(
0.04, decay=0.9, momentum=0.9,
epsilon=0.1).minimize(ae['cost_s'])
elif classifier == 'alexnet_v2':
optimizer_softmax = tf.train.GradientDescentOptimizer(
learning_rate=0.01).minimize(ae['cost_s'])
else:
optimizer_softmax = tf.train.GradientDescentOptimizer(
learning_rate=0.001).minimize(ae['cost_s'])
# We create a session to use the graph together with a GPU declaration.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# config.gpu_options.per_process_gpu_memory_fraction = 0.4
sess = tf.Session(config=config)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
train_writer = tf.summary.FileWriter('./summary', sess.graph)
# This will handle our threaded image pipeline
coord = tf.train.Coordinator()
# Ensure no more changes to graph
tf.get_default_graph().finalize()
# Start up the queues for handling the image pipeline
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
if (os.path.exists(output_path + '/' + ckpt_name + '.index')
or os.path.exists(ckpt_name)):
saver.restore(sess, output_path + '/' + ckpt_name)
print("Model restored")
# Fit all training data
t_i = 0
step_i = 0
batch_i = 0
epoch_i = 0
summary_i = 0
cost = 0
# Test samples of training data from ShapeNet
test_xs_img, test_xs_obj, test_xs_label = sess.run(batch_train)
test_xs_img /= 255.0
test_xs_obj /= 255.0
utils.montage(test_xs_img, output_path + '/train_img.png')
utils.montage(test_xs_obj, output_path + '/train_obj.png')
# Test samples of testing data from ImageNet
test_imagenet_img, _, test_imagenet_label = sess.run(batch_imagenet)
test_imagenet_img /= 255.0
utils.montage(test_imagenet_img, output_path + '/test_imagenet_img.png')
# Test samples of testing data from PASCAL 2012
test_pascal_img, _, test_pascal_label = sess.run(batch_pascal)
test_pascal_img /= 255.0
utils.montage(test_pascal_img, output_path + '/test_pascal_img.png')
# Test samples of testing data from ShapeNet test data
test_shapenet_img, _, test_shapenet_label = sess.run(batch_shapenet)
test_shapenet_img /= 255.0
utils.montage(test_shapenet_img, output_path + '/test_shapenet_img.png')
try:
while not coord.should_stop():
batch_i += 1
step_i += 1
batch_xs_img, batch_xs_obj, batch_xs_label = sess.run(batch_train)
batch_xs_img /= 255.0
batch_xs_obj /= 255.0
# Here we must set corrupt_rec and corrupt_cls as 0 to find a
# proper ratio of variance to feed for variable var_prob.
# We use tanh as non-linear function for ratio of Vars from
# the reconstructed channels and original channels
var_prob = sess.run(ae['var_prob'],
feed_dict={
ae['x']: test_xs_img,
ae['label']: test_xs_label[:, 0],
ae['train']: True,
ae['keep_prob']: 1.0,
ae['corrupt_rec']: 0,
ae['corrupt_cls']: 0
})
# Here is a fast training process
corrupt_rec = np.tanh(0.25 * var_prob)
corrupt_cls = np.tanh(1 - np.tanh(2 * var_prob))
# Optimizing reconstruction network
cost_vae = sess.run(
[ae['cost_vae'], optimizer_vae],
feed_dict={
ae['x']: batch_xs_img,
ae['t']: batch_xs_obj,
ae['label']: batch_xs_label[:, 0],
ae['train']: True,
ae['keep_prob']: keep_prob,
ae['corrupt_rec']: corrupt_rec,
ae['corrupt_cls']: corrupt_cls
})[0]
cost += cost_vae
if softmax:
# Optimizing classification network
cost_s = sess.run(
[ae['cost_s'], optimizer_softmax],
feed_dict={
ae['x']: batch_xs_img,
ae['t']: batch_xs_obj,
ae['label']: batch_xs_label[:, 0],
ae['train']: True,
ae['keep_prob']: keep_prob,
ae['corrupt_rec']: corrupt_rec,
ae['corrupt_cls']: corrupt_cls
})[0]
cost += cost_s
if step_i % img_step == 0:
if variational:
# Plot example reconstructions from latent layer
recon = sess.run(ae['y'],
feed_dict={
ae['z']: zs,
ae['train']: False,
ae['keep_prob']: 1.0,
ae['corrupt_rec']: 0,
ae['corrupt_cls']: 0
})
utils.montage(recon.reshape([-1] + crop_shape),
output_path + '/manifold_%08d.png' % t_i)
# Plot example reconstructions
recon = sess.run(ae['y'],
feed_dict={
ae['x']: test_xs_img,
ae['train']: False,
ae['keep_prob']: 1.0,
ae['corrupt_rec']: 0,
ae['corrupt_cls']: 0
})
utils.montage(recon.reshape([-1] + crop_shape),
output_path + '/recon_%08d.png' % t_i)
"""
filters = sess.run(
ae['Ws'], feed_dict={
ae['x']: test_xs_img,
ae['train']: False,
ae['keep_prob']: 1.0,
ae['corrupt_rec']: 0,
ae['corrupt_cls']: 0})
#for filter_element in filters:
utils.montage_filters(filters[-1],
output_path + '/filter_%08d.png' % t_i)
"""
# Test on ImageNet samples
with open('./list_annotated_imagenet.csv', 'r') as csvfile:
spamreader = csv.reader(csvfile)
rows = list(spamreader)
totalrows = len(rows)
num_batches = np.int_(np.floor(totalrows / batch_size))
accumulated_acc = 0
for index_batch in range(1, num_batches + 1):
test_image, _, test_label = sess.run(batch_imagenet)
test_image /= 255.0
acc, z_codes, sm_codes = sess.run(
[ae['acc'], ae['z'], ae['predictions']],
feed_dict={
ae['x']: test_image,
ae['label']: test_label[:, 0],
ae['train']: False,
ae['keep_prob']: 1.0,
ae['corrupt_rec']: 0,
ae['corrupt_cls']: 0
})
accumulated_acc += acc.tolist().count(True) / acc.size
if index_batch == 1:
z_imagenet = z_codes
sm_imagenet = sm_codes
labels_imagenet = test_label
# Plot example reconstructions
recon = sess.run(ae['y'],
feed_dict={
ae['x']: test_imagenet_img,
ae['train']: False,
ae['keep_prob']: 1.0,
ae['corrupt_rec']: 0,
ae['corrupt_cls']: 0
})
utils.montage(
recon.reshape([-1] + crop_shape),
output_path + '/recon_imagenet_%08d.png' % t_i)
else:
z_imagenet = np.append(z_imagenet, z_codes, axis=0)
sm_imagenet = np.append(sm_imagenet, sm_codes, axis=0)
labels_imagenet = np.append(labels_imagenet,
test_label,
axis=0)
accumulated_acc /= num_batches
print("Accuracy of ImageNet images= %.3f" % (accumulated_acc))
fig = plt.figure()
z_viz, V = pca(z_imagenet, dim_remain=2)
ax = fig.add_subplot(121)
# ax.set_aspect('equal')
ax.scatter(z_viz[:, 0],
z_viz[:, 1],
c=labels_imagenet[:, 0],
alpha=0.4,
cmap='gist_rainbow')
sm_viz, V = pca(sm_imagenet, dim_remain=2)
ax = fig.add_subplot(122)
# ax.set_aspect('equal')
ax.scatter(sm_viz[:, 0],
sm_viz[:, 1],
c=labels_imagenet[:, 0],
alpha=0.4,
cmap='gist_rainbow')
fig.savefig(output_path + '/z_feat_imagenet.png',
transparent=True)
plt.clf()
# Test on PASCAL 2012 samples
with open('./list_annotated_pascal.csv', 'r') as csvfile:
spamreader = csv.reader(csvfile)
rows = list(spamreader)
totalrows = len(rows)
num_batches = np.int_(np.floor(totalrows / batch_size))
accumulated_acc = 0
for index_batch in range(1, num_batches + 1):
test_image, _, test_label = sess.run(batch_pascal)
test_image /= 255.0
acc, z_codes, sm_codes = sess.run(
[ae['acc'], ae['z'], ae['predictions']],
feed_dict={
ae['x']: test_image,
ae['label']: test_label[:, 0],
ae['train']: False,
ae['keep_prob']: 1.0,
ae['corrupt_rec']: 0,
ae['corrupt_cls']: 0
})
accumulated_acc += acc.tolist().count(True) / acc.size
if index_batch == 1:
z_pascal = z_codes
sm_pascal = sm_codes
labels_pascal = test_label
# Plot example reconstructions
recon = sess.run(ae['y'],
feed_dict={
ae['x']: test_pascal_img,
ae['train']: False,
ae['keep_prob']: 1.0,
ae['corrupt_rec']: 0,
ae['corrupt_cls']: 0
})
utils.montage(
recon.reshape([-1] + crop_shape),
output_path + '/recon_pascal_%08d.png' % t_i)
else:
z_pascal = np.append(z_pascal, z_codes, axis=0)
sm_pascal = np.append(sm_pascal, sm_codes, axis=0)
labels_pascal = np.append(labels_pascal,
test_label,
axis=0)
accumulated_acc /= num_batches
print("Accuracy of PASCAL images= %.3f" % (accumulated_acc))
fig = plt.figure()
z_viz, V = pca(z_pascal, dim_remain=2)
ax = fig.add_subplot(121)
# ax.set_aspect('equal')
ax.scatter(z_viz[:, 0],
z_viz[:, 1],
c=labels_pascal[:, 0],
alpha=0.4,
cmap='gist_rainbow')
sm_viz, V = pca(sm_pascal, dim_remain=2)
ax = fig.add_subplot(122)
# ax.set_aspect('equal')
ax.scatter(sm_viz[:, 0],
sm_viz[:, 1],
c=labels_pascal[:, 0],
alpha=0.4,
cmap='gist_rainbow')
fig.savefig(output_path + '/z_feat_pascal.png',
transparent=True)
plt.clf()
# Test on ShapeNet test samples
with open('./list_annotated_img_test.csv', 'r') as csvfile:
spamreader = csv.reader(csvfile)
rows = list(spamreader)
totalrows = len(rows)
num_batches = np.int_(np.floor(totalrows / batch_size))
accumulated_acc = 0
for index_batch in range(1, num_batches + 1):
test_image, _, test_label = sess.run(batch_shapenet)
test_image /= 255.0
acc, z_codes, sm_codes = sess.run(
[ae['acc'], ae['z'], ae['predictions']],
feed_dict={
ae['x']: test_image,
ae['label']: test_label[:, 0],
ae['train']: False,
ae['keep_prob']: 1.0,
ae['corrupt_rec']: 0,
ae['corrupt_cls']: 0
})
accumulated_acc += acc.tolist().count(True) / acc.size
if index_batch == 1:
z_shapenet = z_codes
sm_shapenet = sm_codes
labels_shapenet = test_label
# Plot example reconstructions
recon = sess.run(ae['y'],
feed_dict={
ae['x']: test_shapenet_img,
ae['train']: False,
ae['keep_prob']: 1.0,
ae['corrupt_rec']: 0,
ae['corrupt_cls']: 0
})
utils.montage(
recon.reshape([-1] + crop_shape),
output_path + '/recon_shapenet_%08d.png' % t_i)
else:
z_shapenet = np.append(z_shapenet, z_codes, axis=0)
sm_shapenet = np.append(sm_shapenet, sm_codes, axis=0)
labels_shapenet = np.append(labels_shapenet,
test_label,
axis=0)
accumulated_acc /= num_batches
print("Accuracy of ShapeNet images= %.3f" % (accumulated_acc))
fig = plt.figure()
z_viz, V = pca(z_shapenet, dim_remain=2)
ax = fig.add_subplot(121)
# ax.set_aspect('equal')
ax.scatter(z_viz[:, 0],
z_viz[:, 1],
c=labels_shapenet[:, 0],
alpha=0.4,
cmap='gist_rainbow')
sm_viz, V = pca(sm_shapenet, dim_remain=2)
ax = fig.add_subplot(122)
# ax.set_aspect('equal')
ax.scatter(sm_viz[:, 0],
sm_viz[:, 1],
c=labels_shapenet[:, 0],
alpha=0.4,
cmap='gist_rainbow')
fig.savefig(output_path + '/z_feat_shapenet.png',
transparent=True)
plt.clf()
t_i += 1
if step_i % save_step == 0:
# Save the variables to disk.
# We should set global_step=batch_i if we want several ckpt
saver.save(sess,
output_path + "/" + ckpt_name,
global_step=None,
write_meta_graph=False)
if softmax:
acc = sess.run(ae['acc'],
feed_dict={
ae['x']: test_xs_img,
ae['label']: test_xs_label[:, 0],
ae['train']: False,
ae['keep_prob']: 1.0,
ae['corrupt_rec']: 0,
ae['corrupt_cls']: 0
})
print(
"epoch %d: VAE = %d, SM = %.3f, Acc = %.3f, R_Var = %.3f, Cpt_R = %.3f, Cpt_C = %.3f"
%
(epoch_i, cost_vae, cost_s, acc.tolist().count(True) /
acc.size, var_prob, corrupt_rec, corrupt_cls))
# Summary recording to Tensorboard
summary = sess.run(ae['merged'],
feed_dict={
ae['x']: batch_xs_img,
ae['t']: batch_xs_obj,
ae['label']: batch_xs_label[:, 0],
ae['train']: False,
ae['keep_prob']: keep_prob,
ae['corrupt_rec']: corrupt_rec,
ae['corrupt_cls']: corrupt_cls
})
summary_i += 1
train_writer.add_summary(summary, summary_i)
else:
print("VAE loss = %d" % cost_vae)
if batch_i > (n_files / batch_size):
batch_i = 0
epoch_i += 1
except tf.errors.OutOfRangeError:
print('Done.')
finally:
# One of the threads has issued an exception. So let's tell all the
# threads to shutdown.
coord.request_stop()
# Wait until all threads have finished.
coord.join(threads)
# Clean up the session.
sess.close()
def train_vae(files,
input_shape,
learning_rate=0.0001,
batch_size=100,
n_epochs=50,
n_examples=10,
crop_shape=[64, 64, 3],
crop_factor=0.8,
n_filters=[100, 100, 100, 100],
n_hidden=256,
n_code=50,
convolutional=True,
variational=True,
filter_sizes=[3, 3, 3, 3],
dropout=True,
keep_prob=0.8,
activation=tf.nn.relu,
img_step=100,
save_step=100,
ckpt_name="vae.ckpt"):
"""General purpose training of a (Variational) (Convolutional) Autoencoder.
Supply a list of file paths to images, and this will do everything else.
Parameters
----------
files : list of strings
List of paths to images.
input_shape : list
Must define what the input image's shape is.
learning_rate : float, optional
Learning rate.
batch_size : int, optional
Batch size.
n_epochs : int, optional
Number of epochs.
n_examples : int, optional
Number of example to use while demonstrating the current training
iteration's reconstruction. Creates a square montage, so make
sure int(sqrt(n_examples))**2 = n_examples, e.g. 16, 25, 36, ... 100.
crop_shape : list, optional
Size to centrally crop the image to.
crop_factor : float, optional
Resize factor to apply before cropping.
n_filters : list, optional
Same as VAE's n_filters.
n_hidden : int, optional
Same as VAE's n_hidden.
n_code : int, optional
Same as VAE's n_code.
convolutional : bool, optional
Use convolution or not.
variational : bool, optional
Use variational layer or not.
filter_sizes : list, optional
Same as VAE's filter_sizes.
dropout : bool, optional
Use dropout or not
keep_prob : float, optional
Percent of keep for dropout.
activation : function, optional
Which activation function to use.
img_step : int, optional
How often to save training images showing the manifold and
reconstruction.
save_step : int, optional
How often to save checkpoints.
ckpt_name : str, optional
Checkpoints will be named as this, e.g. 'model.ckpt'
"""
batch = create_input_pipeline(files=files,
batch_size=batch_size,
n_epochs=n_epochs,
crop_shape=crop_shape,
crop_factor=crop_factor,
shape=input_shape)
ae = VAE(input_shape=[None] + crop_shape,
convolutional=convolutional,
variational=variational,
n_filters=n_filters,
n_hidden=n_hidden,
n_code=n_code,
dropout=dropout,
filter_sizes=filter_sizes,
activation=activation)
# Create a manifold of our inner most layer to show
# example reconstructions. This is one way to see
# what the "embedding" or "latent space" of the encoder
# is capable of encoding, though note that this is just
# a random hyperplane within the latent space, and does not
# encompass all possible embeddings.
zs = np.random.uniform(-1.0, 1.0, [4, n_code]).astype(np.float32)
zs = utils.make_latent_manifold(zs, n_examples)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
ae['cost'])
# We create a session to use the graph
sess = tf.Session()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
# This will handle our threaded image pipeline
coord = tf.train.Coordinator()
# Ensure no more changes to graph
tf.get_default_graph().finalize()
# Start up the queues for handling the image pipeline
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
if os.path.exists(ckpt_name + '.index') or os.path.exists(ckpt_name):
saver.restore(sess, ckpt_name)
# Fit all training data
t_i = 0
batch_i = 0
epoch_i = 0
cost = 0
n_files = len(files)
test_xs = sess.run(batch) / 255.0
utils.montage(test_xs, 'test_xs.png')
try:
while not coord.should_stop() and epoch_i < n_epochs:
batch_i += 1
batch_xs = sess.run(batch) / 255.0
train_cost = sess.run([ae['cost'], optimizer],
feed_dict={
ae['x']: batch_xs,
ae['train']: True,
ae['keep_prob']: keep_prob
})[0]
print(batch_i, train_cost)
cost += train_cost
if batch_i % n_files == 0:
print('epoch:', epoch_i)
print('average cost:', cost / batch_i)
cost = 0
batch_i = 0
epoch_i += 1
if batch_i % img_step == 0:
# Plot example reconstructions from latent layer
recon = sess.run(ae['y'],
feed_dict={
ae['z']: zs,
ae['train']: False,
ae['keep_prob']: 1.0
})
utils.montage(recon.reshape([-1] + crop_shape),
'manifold_%08d.png' % t_i)
# Plot example reconstructions
recon = sess.run(ae['y'],
feed_dict={
ae['x']: test_xs,
ae['train']: False,
ae['keep_prob']: 1.0
})
print('reconstruction (min, max, mean):', recon.min(),
recon.max(), recon.mean())
utils.montage(recon.reshape([-1] + crop_shape),
'reconstruction_%08d.png' % t_i)
t_i += 1
if batch_i % save_step == 0:
# Save the variables to disk.
saver.save(sess,
"./" + ckpt_name,
global_step=batch_i,
write_meta_graph=False)
except tf.errors.OutOfRangeError:
print('Done.')
finally:
# One of the threads has issued an exception. So let's tell all the
# threads to shutdown.
coord.request_stop()
# Wait until all threads have finished.
coord.join(threads)
# Clean up the session.
sess.close()
def train_vaegan(files,
learning_rate=0.00001,
batch_size=64,
n_epochs=250,
n_examples=10,
input_shape=[218, 178, 3],
crop_shape=[64, 64, 3],
crop_factor=0.8,
n_filters=[100, 100, 100, 100],
n_hidden=None,
n_code=128,
convolutional=True,
variational=True,
filter_sizes=[3, 3, 3, 3],
activation=tf.nn.elu,
ckpt_name="vaegan.ckpt"):
"""Summary
Parameters
----------
files : TYPE
Description
learning_rate : float, optional
Description
batch_size : int, optional
Description
n_epochs : int, optional
Description
n_examples : int, optional
Description
input_shape : list, optional
Description
crop_shape : list, optional
Description
crop_factor : float, optional
Description
n_filters : list, optional
Description
n_hidden : int, optional
Description
n_code : int, optional
Description
convolutional : bool, optional
Description
variational : bool, optional
Description
filter_sizes : list, optional
Description
activation : TYPE, optional
Description
ckpt_name : str, optional
Description
No Longer Returned
------------------
name : TYPE
Description
"""
ae = VAEGAN(input_shape=[None] + crop_shape,
convolutional=convolutional,
variational=variational,
n_filters=n_filters,
n_hidden=n_hidden,
n_code=n_code,
filter_sizes=filter_sizes,
activation=activation)
batch = create_input_pipeline(files=files,
batch_size=batch_size,
n_epochs=n_epochs,
crop_shape=crop_shape,
crop_factor=crop_factor,
shape=input_shape)
zs = np.random.randn(4, n_code).astype(np.float32)
zs = utils.make_latent_manifold(zs, n_examples)
opt_enc = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
ae['loss_enc'],
var_list=[
var_i for var_i in tf.trainable_variables()
if var_i.name.startswith('encoder')
])
opt_gen = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
ae['loss_gen'],
var_list=[
var_i for var_i in tf.trainable_variables()
if var_i.name.startswith('generator')
])
opt_dis = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
ae['loss_dis'],
var_list=[
var_i for var_i in tf.trainable_variables()
if var_i.name.startswith('discriminator')
])
sess = tf.Session()
saver = tf.train.Saver()
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
tf.get_default_graph().finalize()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
if os.path.exists(ckpt_name + '.index') or os.path.exists(ckpt_name):
saver.restore(sess, ckpt_name)
print("VAE model restored.")
t_i = 0
batch_i = 0
epoch_i = 0
equilibrium = 0.693
margin = 0.4
n_files = len(files)
test_xs = sess.run(batch) / 255.0
utils.montage(test_xs, 'test_xs.png')
try:
while not coord.should_stop() and epoch_i < n_epochs:
if batch_i % (n_files // batch_size) == 0:
batch_i = 0
epoch_i += 1
print('---------- EPOCH:', epoch_i)
batch_i += 1
batch_xs = sess.run(batch) / 255.0
batch_zs = np.random.randn(batch_size, n_code).astype(np.float32)
real_cost, fake_cost, _ = sess.run(
[ae['loss_real'], ae['loss_fake'], opt_enc],
feed_dict={
ae['x']: batch_xs,
ae['gamma']: 0.5
})
real_cost = -np.mean(real_cost)
fake_cost = -np.mean(fake_cost)
print('real:', real_cost, '/ fake:', fake_cost)
gen_update = True
dis_update = True
if real_cost > (equilibrium + margin) or \
fake_cost > (equilibrium + margin):
gen_update = False
if real_cost < (equilibrium - margin) or \
fake_cost < (equilibrium - margin):
dis_update = False
if not (gen_update or dis_update):
gen_update = True
dis_update = True
if gen_update:
sess.run(opt_gen,
feed_dict={
ae['x']: batch_xs,
ae['z_samp']: batch_zs,
ae['gamma']: 0.5
})
if dis_update:
sess.run(opt_dis,
feed_dict={
ae['x']: batch_xs,
ae['z_samp']: batch_zs,
ae['gamma']: 0.5
})
if batch_i % 50 == 0:
# Plot example reconstructions from latent layer
recon = sess.run(ae['x_tilde'], feed_dict={ae['z']: zs})
print('recon:', recon.min(), recon.max())
recon = np.clip(recon / recon.max(), 0, 1)
utils.montage(recon.reshape([-1] + crop_shape),
'imgs/manifold_%08d.png' % t_i)
# Plot example reconstructions
recon = sess.run(ae['x_tilde'], feed_dict={ae['x']: test_xs})
print('recon:', recon.min(), recon.max())
recon = np.clip(recon / recon.max(), 0, 1)
utils.montage(recon.reshape([-1] + crop_shape),
'imgs/reconstruction_%08d.png' % t_i)
t_i += 1
if batch_i % 100 == 0:
# Save the variables to disk.
save_path = saver.save(sess,
ckpt_name,
global_step=batch_i,
write_meta_graph=False)
print("Model saved in file: %s" % save_path)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# One of the threads has issued an exception. So let's tell all the
# threads to shutdown.
coord.request_stop()
# Wait until all threads have finished.
coord.join(threads)
# Clean up the session.
sess.close()
def test_mnist(n_epochs=10):
"""Train an autoencoder on MNIST.
This function will train an autoencoder on MNIST and also
save many image files during the training process, demonstrating
the latent space of the inner most dimension of the encoder,
as well as reconstructions of the decoder.
"""
# load MNIST
n_code = 2
mnist = MNIST(split=[0.8, 0.1, 0.1])
ae = VAE(input_shape=[None, 784], n_filters=[512, 256],
n_hidden=64, n_code=n_code, activation=tf.nn.sigmoid,
convolutional=False, variational=True)
n_examples = 100
zs = np.random.uniform(
-1.0, 1.0, [4, n_code]).astype(np.float32)
zs = utils.make_latent_manifold(zs, n_examples)
learning_rate = 0.02
optimizer = tf.train.AdamOptimizer(
learning_rate=learning_rate).minimize(ae['cost'])
# We create a session to use the graph
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Fit all training data
t_i = 0
batch_i = 0
batch_size = 200
test_xs = mnist.test.images[:n_examples]
utils.montage(test_xs.reshape((-1, 28, 28)), 'test_xs.png')
for epoch_i in range(n_epochs):
train_i = 0
train_cost = 0
for batch_xs, _ in mnist.train.next_batch(batch_size):
train_cost += sess.run([ae['cost'], optimizer], feed_dict={
ae['x']: batch_xs, ae['train']: True, ae['keep_prob']: 1.0})[0]
train_i += 1
if batch_i % 10 == 0:
# Plot example reconstructions from latent layer
recon = sess.run(
ae['y'], feed_dict={
ae['z']: zs,
ae['train']: False,
ae['keep_prob']: 1.0})
m = utils.montage(recon.reshape((-1, 28, 28)),
'manifold_%08d.png' % t_i)
# Plot example reconstructions
recon = sess.run(
ae['y'], feed_dict={ae['x']: test_xs,
ae['train']: False,
ae['keep_prob']: 1.0})
m = utils.montage(recon.reshape(
(-1, 28, 28)), 'reconstruction_%08d.png' % t_i)
t_i += 1
batch_i += 1
valid_i = 0
valid_cost = 0
for batch_xs, _ in mnist.valid.next_batch(batch_size):
valid_cost += sess.run([ae['cost']], feed_dict={
ae['x']: batch_xs, ae['train']: False, ae['keep_prob']: 1.0})[0]
valid_i += 1
print('train:', train_cost / train_i, 'valid:', valid_cost / valid_i)
def train_vae(files,
input_shape,
learning_rate=0.0001,
batch_size=100,
n_epochs=50,
n_examples=10,
crop_shape=[64, 64, 3],
crop_factor=0.8,
n_filters=[100, 100, 100, 100],
n_hidden=256,
n_code=50,
convolutional=True,
variational=True,
filter_sizes=[3, 3, 3, 3],
dropout=True,
keep_prob=0.8,
activation=tf.nn.relu,
img_step=100,
save_step=100,
ckpt_name="vae.ckpt"):
"""General purpose training of a (Variational) (Convolutional) Autoencoder.
Supply a list of file paths to images, and this will do everything else.
Parameters
----------
files : list of strings
List of paths to images.
input_shape : list
Must define what the input image's shape is.
learning_rate : float, optional
Learning rate.
batch_size : int, optional
Batch size.
n_epochs : int, optional
Number of epochs.
n_examples : int, optional
Number of example to use while demonstrating the current training
iteration's reconstruction. Creates a square montage, so make
sure int(sqrt(n_examples))**2 = n_examples, e.g. 16, 25, 36, ... 100.
crop_shape : list, optional
Size to centrally crop the image to.
crop_factor : float, optional
Resize factor to apply before cropping.
n_filters : list, optional
Same as VAE's n_filters.
n_hidden : int, optional
Same as VAE's n_hidden.
n_code : int, optional
Same as VAE's n_code.
convolutional : bool, optional
Use convolution or not.
variational : bool, optional
Use variational layer or not.
filter_sizes : list, optional
Same as VAE's filter_sizes.
dropout : bool, optional
Use dropout or not
keep_prob : float, optional
Percent of keep for dropout.
activation : function, optional
Which activation function to use.
img_step : int, optional
How often to save training images showing the manifold and
reconstruction.
save_step : int, optional
How often to save checkpoints.
ckpt_name : str, optional
Checkpoints will be named as this, e.g. 'model.ckpt'
"""
batch = create_input_pipeline(
files=files,
batch_size=batch_size,
n_epochs=n_epochs,
crop_shape=crop_shape,
crop_factor=crop_factor,
shape=input_shape)
ae = VAE(input_shape=[None] + crop_shape,
convolutional=convolutional,
variational=variational,
n_filters=n_filters,
n_hidden=n_hidden,
n_code=n_code,
dropout=dropout,
filter_sizes=filter_sizes,
activation=activation)
# Create a manifold of our inner most layer to show
# example reconstructions. This is one way to see
# what the "embedding" or "latent space" of the encoder
# is capable of encoding, though note that this is just
# a random hyperplane within the latent space, and does not
# encompass all possible embeddings.
zs = np.random.uniform(
-1.0, 1.0, [4, n_code]).astype(np.float32)
zs = utils.make_latent_manifold(zs, n_examples)
optimizer = tf.train.AdamOptimizer(
learning_rate=learning_rate).minimize(ae['cost'])
# We create a session to use the graph
sess = tf.Session()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
# This will handle our threaded image pipeline
coord = tf.train.Coordinator()
# Ensure no more changes to graph
tf.get_default_graph().finalize()
# Start up the queues for handling the image pipeline
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
if os.path.exists(ckpt_name + '.index') or os.path.exists(ckpt_name):
saver.restore(sess, ckpt_name)
# Fit all training data
t_i = 0
batch_i = 0
epoch_i = 0
cost = 0
n_files = len(files)
test_xs = sess.run(batch) / 255.0
utils.montage(test_xs, 'test_xs.png')
try:
while not coord.should_stop() and epoch_i < n_epochs:
batch_i += 1
batch_xs = sess.run(batch) / 255.0
train_cost = sess.run([ae['cost'], optimizer], feed_dict={
ae['x']: batch_xs, ae['train']: True,
ae['keep_prob']: keep_prob})[0]
print(batch_i, train_cost)
cost += train_cost
if batch_i % n_files == 0:
print('epoch:', epoch_i)
print('average cost:', cost / batch_i)
cost = 0
batch_i = 0
epoch_i += 1
if batch_i % img_step == 0:
# Plot example reconstructions from latent layer
recon = sess.run(
ae['y'], feed_dict={
ae['z']: zs,
ae['train']: False,
ae['keep_prob']: 1.0})
utils.montage(recon.reshape([-1] + crop_shape),
'manifold_%08d.png' % t_i)
# Plot example reconstructions
recon = sess.run(
ae['y'], feed_dict={ae['x']: test_xs,
ae['train']: False,
ae['keep_prob']: 1.0})
print('reconstruction (min, max, mean):',
recon.min(), recon.max(), recon.mean())
utils.montage(recon.reshape([-1] + crop_shape),
'reconstruction_%08d.png' % t_i)
t_i += 1
if batch_i % save_step == 0:
# Save the variables to disk.
saver.save(sess, "./" + ckpt_name,
global_step=batch_i,
write_meta_graph=False)
except tf.errors.OutOfRangeError:
print('Done.')
finally:
# One of the threads has issued an exception. So let's tell all the
# threads to shutdown.
coord.request_stop()
# Wait until all threads have finished.
coord.join(threads)
# Clean up the session.
sess.close()
def train_vaegan(files,
learning_rate=0.00001,
batch_size=64,
n_epochs=250,
n_examples=10,
input_shape=[218, 178, 3],
crop_shape=[64, 64, 3],
crop_factor=0.8,
n_filters=[100, 100, 100, 100],
n_hidden=None,
n_code=128,
convolutional=True,
variational=True,
filter_sizes=[3, 3, 3, 3],
activation=tf.nn.elu,
ckpt_name="vaegan.ckpt"):
"""Summary
Parameters
----------
files : TYPE
Description
learning_rate : float, optional
Description
batch_size : int, optional
Description
n_epochs : int, optional
Description
n_examples : int, optional
Description
input_shape : list, optional
Description
crop_shape : list, optional
Description
crop_factor : float, optional
Description
n_filters : list, optional
Description
n_hidden : int, optional
Description
n_code : int, optional
Description
convolutional : bool, optional
Description
variational : bool, optional
Description
filter_sizes : list, optional
Description
activation : TYPE, optional
Description
ckpt_name : str, optional
Description
No Longer Returned
------------------
name : TYPE
Description
"""
ae = VAEGAN(
input_shape=[None] + crop_shape,
convolutional=convolutional,
variational=variational,
n_filters=n_filters,
n_hidden=n_hidden,
n_code=n_code,
filter_sizes=filter_sizes,
activation=activation)
batch = create_input_pipeline(
files=files,
batch_size=batch_size,
n_epochs=n_epochs,
crop_shape=crop_shape,
crop_factor=crop_factor,
shape=input_shape)
zs = np.random.randn(4, n_code).astype(np.float32)
zs = utils.make_latent_manifold(zs, n_examples)
opt_enc = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
ae['loss_enc'],
var_list=[
var_i for var_i in tf.trainable_variables()
if var_i.name.startswith('encoder')
])
opt_gen = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
ae['loss_gen'],
var_list=[
var_i for var_i in tf.trainable_variables()
if var_i.name.startswith('generator')
])
opt_dis = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
ae['loss_dis'],
var_list=[
var_i for var_i in tf.trainable_variables()
if var_i.name.startswith('discriminator')
])
sess = tf.Session()
saver = tf.train.Saver()
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
tf.get_default_graph().finalize()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
if os.path.exists(ckpt_name + '.index') or os.path.exists(ckpt_name):
saver.restore(sess, ckpt_name)
print("VAE model restored.")
t_i = 0
batch_i = 0
epoch_i = 0
equilibrium = 0.693
margin = 0.4
n_files = len(files)
test_xs = sess.run(batch) / 255.0
utils.montage(test_xs, 'test_xs.png')
try:
while not coord.should_stop() and epoch_i < n_epochs:
if batch_i % (n_files // batch_size) == 0:
batch_i = 0
epoch_i += 1
print('---------- EPOCH:', epoch_i)
batch_i += 1
batch_xs = sess.run(batch) / 255.0
batch_zs = np.random.randn(batch_size, n_code).astype(np.float32)
real_cost, fake_cost, _ = sess.run(
[ae['loss_real'], ae['loss_fake'], opt_enc],
feed_dict={ae['x']: batch_xs,
ae['gamma']: 0.5})
real_cost = -np.mean(real_cost)
fake_cost = -np.mean(fake_cost)
print('real:', real_cost, '/ fake:', fake_cost)
gen_update = True
dis_update = True
if real_cost > (equilibrium + margin) or \
fake_cost > (equilibrium + margin):
gen_update = False
if real_cost < (equilibrium - margin) or \
fake_cost < (equilibrium - margin):
dis_update = False
if not (gen_update or dis_update):
gen_update = True
dis_update = True
if gen_update:
sess.run(
opt_gen,
feed_dict={
ae['x']: batch_xs,
ae['z_samp']: batch_zs,
ae['gamma']: 0.5
})
if dis_update:
sess.run(
opt_dis,
feed_dict={
ae['x']: batch_xs,
ae['z_samp']: batch_zs,
ae['gamma']: 0.5
})
if batch_i % 50 == 0:
# Plot example reconstructions from latent layer
recon = sess.run(ae['x_tilde'], feed_dict={ae['z']: zs})
print('recon:', recon.min(), recon.max())
recon = np.clip(recon / recon.max(), 0, 1)
utils.montage(
recon.reshape([-1] + crop_shape),
'imgs/manifold_%08d.png' % t_i)
# Plot example reconstructions
recon = sess.run(ae['x_tilde'], feed_dict={ae['x']: test_xs})
print('recon:', recon.min(), recon.max())
recon = np.clip(recon / recon.max(), 0, 1)
utils.montage(
recon.reshape([-1] + crop_shape),
'imgs/reconstruction_%08d.png' % t_i)
t_i += 1
if batch_i % 100 == 0:
# Save the variables to disk.
save_path = saver.save(
sess,
ckpt_name,
global_step=batch_i,
write_meta_graph=False)
print("Model saved in file: %s" % save_path)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# One of the threads has issued an exception. So let's tell all the
# threads to shutdown.
coord.request_stop()
# Wait until all threads have finished.
coord.join(threads)
# Clean up the session.
sess.close()
