How to save and restore the convolution autoencoder neural network model - python-3.x

Question

I use the convolutional autoencoder neural network method to train my model and then save it, but when I restore my model to reconstruct the image which is similar to the training image, the reconstruction result is very bad and the loss is large. I am not sure if I am wrong with saving and reading files.
Training model and save it!
#--------------------------------------------------------------------------
x = tf.placeholder(tf.float32, [None, dim], name = "X")
y = tf.placeholder(tf.float32, [None, dim], name = "Y")
keepprob = tf.placeholder(tf.float32, name = "K")
pred = cae(x, weights, biases, keepprob, imgsize)["out"]
cost = tf.reduce_sum(tf.square(cae(x, weights, biases, keepprob,imgsize)["out"] - tf.reshape(y, shape=[-1, imgsize, imgsize, 1])))
learning_rate = 0.01
optm = tf.train.AdamOptimizer(learning_rate).minimize(cost)
#--------------------------------------------------------------------------
sess = tf.Session()
save_model = os.path.join(PATH,'temp_saved_model')
saver = tf.train.Saver()
tf.add_to_collection("COST", cost)
tf.add_to_collection("PRED", pred)
sess.run(tf.global_variables_initializer())
mean_img = np.zeros((dim))
batch_size = 100
n_epochs = 1000
for epoch_i in range(n_epochs):
for batch_i in range(ntrain // batch_size):
trainbatch = np.array(train)
trainbatch = np.array([img - mean_img for img in trainbatch])
sess.run(optm, feed_dict={x: trainbatch, y: trainbatch, keepprob: 1.})
save_path = saver.save(sess, save_model)
print('Model saved in file: %s' %save_path)
sess.close()
Restoring the model and try to reconstruct the image.
tf.reset_default_graph()
save_model = os.path.join(PATH + 'SaveModel/','temp_saved_model.meta')
imgsize = 64
dim = imgsize * imgsize
mean_img = np.zeros((dim))
with tf.Session() as sess:
saver = tf.train.import_meta_graph(save_model)
saver.restore(sess, tf.train.latest_checkpoint(PATH + 'SaveModel/'))
cost = tf.get_collection("COST")[0]
pred = tf.get_collection("PRED")[0]
graph = tf.get_default_graph()
x = graph.get_tensor_by_name("X:0")
y = graph.get_tensor_by_name("Y:0")
k = graph.get_tensor_by_name("K:0")
for i in range(10):
test_xs = np.array(data)
test = load_image(test_xs, imgsize)
test = np.array([img - mean_img for img in test])
print ("[%02d/%02d] cost: %.4f" % (i, 10, sess.run(cost, feed_dict={x: test, y: test, K: 1.})))
The loss value in the training process is 1.321..., but the reconstruction loss is 16545.10441... Is there something wrong in my code?

Answer 1

First make sure that Your Restore and Save functions are in Different files.
There are a few problems that I have debugged so far,
keepprob changes from 'K' to 'k' while building graph after restore.
You are facing same Images as Logits and labels (Doesn't make sense until you are trying to learn an Identity function)
You are calculating training cost before saving the model and validation/test cost after restoring the model.
Your code in saver
recon = sess.run(pred, feed_dict={x: testbatch, keepprob: 1.})
fig, axs = plt.subplots(2, n_examples, figsize=(15, 4))
for example_i in range(5):
axs[0][example_i].matshow(np.reshape(testbatch[example_i, :], (imgsize, imgsize)), cmap=plt.get_cmap('gray'))
axs[1][example_i].matshow(np.reshape(np.reshape(recon[example_i, ...], (dim,)) + mean_img, (imgsize, imgsize)), cmap=plt.get_cmap('gray'))
plt.show()
Your code in restore function
recon = sess.run(pred, feed_dict={x: test, k: 1.})
cost = sess.run(cost, feed_dict={x: test, y: test, k: 1.})
if (i % 2) == 0:
fig, axs = plt.subplots(2, n_examples, figsize=(15, 4))
for example_i in range(n_examples):
axs[0][example_i].matshow(np.reshape(test[example_i, :], (imgsize, imgsize)), cmap=plt.get_cmap('gray'))
axs[1][example_i].matshow(np.reshape(np.reshape(recon[example_i, ...], (dim,)) + mean_img, (imgsize, imgsize)), cmap=plt.get_cmap('gray'))
plt.show()
Also nowhere in your code are you printing/plotting cost even in your recover module you are plotting recon variable
If you are trying to test autoencoder-decoder pair, to generate the original Image, your model is a bit too small(Shallow), If that makes sense, try implementing it, if you are confused, check out this link. https://pgaleone.eu/neural-networks/deep-learning/2016/12/13/convolutional-autoencoders-in-tensorflow/
And in any case, feel free to add comments for further clarification.