def get_celeb_images():
print('##Start get_celeb_images##')
dirname = "C:\\Users\\rahmanim\\Documents\\1TF-Gits\\ImagesFromTheInternet"
filenames = [os.path.join(dirname, fname) for fname in os.listdir(dirname)]
# Make sure we have exactly 100 image files!
filenames = filenames[:100]
assert (len(filenames) == 100)
#print(filenames)
# Read every filename as an RGB image
imgs = [plt.imread(fname)[..., :3] for fname in filenames]
# Crop every image to a square
imgs = [utils.imcrop_tosquare(img_i) for img_i in imgs]
# Then resize the square image to 100 x 100 pixels; mode='reflect'
imgs = [resize(img_i, (100, 100), mode='reflect') for img_i in imgs]
# Finally make our list of 3-D images a 4-D array with the first dimension the number of images:
imgs = np.array(imgs).astype(np.float32)
# Plot the resulting dataset:
# Make sure you "run" this cell after you create your `imgs` variable as a 4-D array!
# Make sure we have a 100 x 100 x 100 x 3 dimension array
assert (imgs.shape == (100, 100, 100, 3))
plt.figure(figsize=(10, 10))
plt.imshow(utils.montage(imgs, saveto='dataset.png'))
plt.title('Dataset of 100 images')
#plt.show()
return imgs
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,
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()
std_img = np.mean(std_img, axis=2) #plt.imshow(std_img) #plt.show() #plt.imshow(ds.X[0]) #plt.show() #print(ds.X.shape) #reset_default_graph() # X is the list of all the images imgs = images_to_gray(ds.X[:100]) # And we'll create a placeholder in the tensorflow graph that will be able to get any number of n_feature inputs. # Then create a montage and draw the montage plt.imshow(montage(imgs), cmap='gray') plt.show() n_features = 10000 mean_img = np.mean(imgs, axis=0) std_img = np.std(imgs, axis=0) plt.imshow(mean_img) plt.show() plt.imshow(std_img) plt.show() X = tf.placeholder(tf.float32, [None, n_features]) X_tensor = tf.reshape(X, [-1, 100, 100, 1]) n_filters = [16, 16, 16] filter_sizes = [4, 4, 4] current_input = X_tensor
def test_montage():
assert(utils.slice_montage(
utils.montage(np.zeros((100, 3, 3, 3))), 3, 3, 100).shape == (100, 3, 3, 3))
gifs.append(img)
return gifs
# <a name="code-1"></a>
# ## Code
#
# Below, I've shown code for loading the first 100 celeb files. Run through the next few cells to see how this works with the celeb dataset, and then come back here and replace the `imgs` variable with your own set of images. For instance, you can try your entire sorted dataset from Session 1 as an N x H x W x C array. Explore!
#
# <h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>
# In[ ]:
celeb_imgs = utils.get_celeb_imgs()
plt.figure(figsize=(10, 10))
plt.imshow(utils.montage(celeb_imgs).astype(np.uint8))
# Replace this with your own dataset of N x H x W x C!
# It doesn't have to be 100 images, explore!
imgs = np.array(celeb_imgs).copy()
# Explore changing the parameters of the `train` function and your own dataset of images. Note, you do not have to use the dataset from the last assignment! Explore different numbers of images, whatever you prefer.
#
# <h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>
# In[ ]:
# Change the parameters of the train function and
# explore changing the dataset
gifs = train(imgs=imgs)
def create_vanilla_auto_encoder(n_features, dimensions):
ds = MNIST()
# X is the list of all the images in MNIST dataset
imgs = ds.X[:1000].reshape((-1, 28, 28))
# Then create a montage and draw the montage
plt.imshow(montage(imgs), cmap='gray')
plt.show()
mean_img = np.mean(ds.X, axis=0)
std_img = np.std(imgs, axis=0)
X = tf.placeholder(tf.float32, [None, n_features])
current_input = X
n_input = n_features
Ws = []
for layer_i, dimension_i in enumerate(dimensions, start=1):
with tf.variable_scope("encoder/layer/{}".format(layer_i)):
w = tf.get_variable(name='W',
shape=[n_input, dimension_i],
initializer=tf.random_normal_initializer(
mean=0.0, stddev=2.0))
b = tf.get_variable(name='b',
shape=[dimension_i],
dtype=tf.float32,
initializer=tf.constant_initializer(0.0))
h = tf.nn.bias_add(name='h',
value=tf.matmul(current_input, w),
bias=b)
current_input = tf.nn.relu(h)
Ws.append(w)
n_input = dimension_i
Ws = Ws[::-1]
dimensions = dimensions[::-1][1:] + [n_features]
print('dimensions=', dimensions)
for layer_i, dimension_i in enumerate(dimensions):
with tf.variable_scope("decoder/layer/{}".format(layer_i)):
w = tf.transpose(Ws[layer_i])
b = tf.get_variable(name='b',
shape=[dimension_i],
dtype=tf.float32,
initializer=tf.constant_initializer(0.0))
print('current_input= ', current_input)
print('w = ', w)
h = tf.nn.bias_add(name='h',
value=tf.matmul(current_input, w),
bias=b)
current_input = tf.nn.relu(h)
n_input = dimension_i
Y = current_input
cost = tf.reduce_mean(tf.squared_difference(X, Y), 1)
print('cost.getshape', cost.get_shape())
cost = tf.reduce_mean(cost)
learning_rate = 0.01
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
batch_size = 100
n_epochs = 60
# We'll try to reconstruct the same first 100 images and show how The network does over the course of training.
examples = ds.X[:100]
mean_img = np.mean(examples, axis=0)
#recon0 = np.clip(examples.reshape((-1, 28, 28)), 0, 255)
#img_or = montage(recon0).astype(np.uint8)
#img_or.append('0')
#gif.build_gif(img_or, saveto='example.{}.gif'.format(np.random.rand()), cmap='gray')
#plt.show()
# We'll store the reconstructions in a list
imgs = []
fig, ax = plt.subplots(1, 1)
for epoch_i in range(n_epochs):
for batch_X, _ in ds.train.next_batch():
sess.run(optimizer, feed_dict={X: batch_X - mean_img})
recon = sess.run(Y, feed_dict={X: examples - mean_img})
recon = np.clip((recon + mean_img).reshape((-1, 28, 28)), 0, 255)
img_i = montage(recon).astype(np.uint8)
imgs.append(img_i)
ax.imshow(img_i, cmap='gray')
fig.canvas.draw()
#plt.imshow(img_i, cmap='gray')
#plt.show()
print(epoch_i, sess.run(cost, feed_dict={X: batch_X - mean_img}))
gif.build_gif(imgs,
saveto='ae6-learning0.{}.gif'.format(np.random.rand()),
cmap='gray')
ipyd.Image(url='ae.gif?{}'.format(np.random.rand()), height=500, width=500)
return Y
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()
# (which stores the mean and standard deviation!)
def deprocess(norm_img, ds):
img = norm_img * ds.std() + ds.mean()
return img
direc = '/home/yida/Documents/buildboat/slic_superpixel/data/annotated_img/aeroplane'
myown_img = get_myown_imgs(direc)
direc = '/home/yida/Documents/buildboat/slic_superpixel/data/annotated_obj/aeroplane'
myown_obj = get_myown_imgs(direc)
# Then resize the square image to 100 x 100 pixels
myown_img = [resize(img_i, (100, 100, 3)) for img_i in myown_img]
myown_obj = [resize(img_i, (100, 100, 3)) for img_i in myown_obj]
plt.figure(figsize=(10, 10))
plt.imshow(utils.montage(myown_img))
# Then convert the list of images to a 4d array (e.g. use np.array to convert a list to a 4d array):
Xs = np.array(myown_img).copy()*255
print(Xs.shape)
assert(Xs.ndim == 4 and Xs.shape[1] <= 100 and Xs.shape[2] <= 100)
ds_img = datasets.Dataset(Xs)
# Then convert the list of images to a 4d array (e.g. use np.array to convert a list to a 4d array):
Xs = np.array(myown_obj).copy()*255
print(Xs.shape)
assert(Xs.ndim == 4 and Xs.shape[1] <= 100 and Xs.shape[2] <= 100)
def test_montage():
assert(utils.montage(np.zeros((100, 32, 32, 3))).shape == (331, 331, 3))
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()
refimage = image_prep("girl.jpg", ".\\img\\")
## Put pixel locations and pixel values in xs and xy
xs, ys = split_image(refimage)
## normalize xs and xy
norm_xs = normalize_std_score(xs.astype(np.float32))
norm_ys = normalize_feature_scaling(ys.astype(np.float32))
## Create the neural network
y_pred = create_nn()
print(y_pred)
## Write the cost function (Put part 3 to code)
celeb_imgs = get_celeb_images()
plt.figure(figsize=(10, 10))
plt.imshow(utils.montage(celeb_imgs).astype(np.uint8))
#plt.show()
# It doesn't have to be 100 images, explore!
imgs = np.array(celeb_imgs).copy()
# Change the parameters of the train function and
# explore changing the dataset
gifs = train(imgs=imgs)
montage_gifs = [
np.clip(utils.montage((m * 127.5) + 127.5), 0, 255).astype(np.uint8)
for m in gifs
]
plt.imshow(utils.montage(montage_gifs, saveto='montage_gifs.gif'))
imgs = [utils.imcrop_tosquare(img_i) for img_i in imgs]
# Then resize the square image to 100 x 100 pixels
from skimage.transform import resize
imgs = [resize(img_i, (100, 100)) for img_i in imgs]
# Finally make our list of 3-D images a 4-D array with the first dimension the number of images:
import numpy as np
imgs = np.array(imgs).astype(np.float32)
# Plot the resulting dataset:
# Make sure you "run" this cell after you create your `imgs` variable as a 4-D array!
# Make sure we have a 100 x 100 x 100 x 3 dimension array
assert (imgs.shape == (100, 100, 100, 3))
plt.figure(figsize=(10, 10))
plt.imshow(utils.montage(imgs, saveto='dataset.png'))
plt.show()
img_mean = np.mean(imgs, axis=0)
plt.figure()
plt.imshow(img_mean)
plt.title('Mean image')
plt.show()
img_stddev = np.std(imgs, axis=0)
plt.figure()
plt.imshow(img_stddev)
plt.title('Standard dev image')
plt.show()
'''
#
filenames = [os.path.join(dirname, fname) for fname in os.listdir(dirname)] filenames = filenames[:100] assert (len(filenames) == 100) # Read the files as images imgs = [plt.imread(fname)[:, :, :3] for fname in filenames] imgs = [resize(img_i, (100, 100)) for img_i in imgs] imgs = np.array(imgs).astype(np.float32) assert (imgs.shape == (100, 100, 100, 3)) plt.figure(figsize=(10, 10)) plt.imshow(utils.montage(imgs, saveto='dataset.png')) ########## sess = tf.Session() mean_img_op = tf.reduce_mean(imgs, reduction_indices=0, name='mean') mean_img = sess.run(mean_img_op) assert (mean_img.shape == (100, 100, 3)) plt.figure(figsize=(10, 10)) plt.imshow(mean_img) plt.imsave(arr=mean_img, fname='mean.png') ### mean_img_4d = tf.reshape(mean_img, [1, 100, 100, 3])
def test_montage():
assert (utils.montage(np.zeros((100, 32, 32, 3))).shape == (331, 331, 3))
Y = current_input
Y = tf.reshape(Y, [-1, n_features])
cost = tf.reduce_mean(tf.reduce_mean(tf.squared_difference(X, Y), 1))
learning_rate = 0.001
# pass learning rate and cost to optimize
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
# Session to manage vars/train
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Some parameters for training
batch_size = 100
n_epochs = 5
# We'll try to reconstruct the same first 100 images and show how
# The network does over the course of training.
examples = ds.X[:100]
# We'll store the reconstructions in a list
gif_imgs = []
for epoch_i in range(n_epochs):
for batch_X, _ in ds.train.next_batch():
sess.run(optimizer, feed_dict={X: batch_X - mean_img})
recon = sess.run(Y, feed_dict={X: examples - mean_img})
recon = np.clip((recon + mean_img).reshape((-1, 28, 28)), 0, 255)
img_i = montage(recon).astype(np.uint8)
gif_imgs.append(img_i)
print(epoch_i)
gif.build_gif(imgs, saveto='conv-ae.gif', cmap='gray')
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()
imgs = [utils.imcrop_tosquare(img_i) for img_i in imgs] # Then resize the square image to 100 x 100 pixels imgs = [resize(img_i, (100, 100)) for img_i in imgs] # Finally make our list of 3-D images a 4-D array with the first dimension the number of images: imgs = np.array(imgs).astype(np.float32) # In[ ]: # Plot the resulting dataset: # Make sure you "run" this cell after you create your `imgs` variable as a 4-D array! # Make sure we have a 154 x 100 x 100 x 3 dimension array assert (imgs.shape == (144, 100, 100, 3)) plt.figure(figsize=(10, 10)) plt.imshow(utils.montage(imgs, saveto='dataset.png')) # <a name="part-two---compute-the-mean"></a> # # Part Two - Compute the Mean # # <a name="instructions-1"></a> # ## Instructions # # First use Tensorflow to define a session. Then use Tensorflow to create an operation which takes your 4-d array and calculates the mean color image (100 x 100 x 3) using the function `tf.reduce_mean`. Have a look at the documentation for this function to see how it works in order to get the mean of every pixel and get an image of (100 x 100 x 3) as a result. You'll then calculate the mean image by running the operation you create with your session (e.g. <code>sess.run(...)</code>). Finally, plot the mean image, save it, and then include this image in your zip file as <b>mean.png</b>. # # <a name="code-1"></a> # ## Code # # <h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3> # In[ ]: