Tensorflow: How to predict a single image from the model trained? - tensorflow

Question

I am new to tensorflow and I am trying to build an image classifier. I have successfully created the model and I am trying to predict a single image after restoring the model. I have gone through various tutorials (https://github.com/sankit1/cv-tricks.com/blob/master/Tensorflow-tutorials/tutorial-2-image-classifier/predict.py) but I can't figure out the feed-dict thing in my code. I am stuck at predict fnction after loading the saved model. Can someone please help me and tell me what to do after loading all the variables from the saved model?
This is the train function which returns the parameters and save them in a model.
def trainModel(train, test, learning_rate=0.0001, num_epochs=2, minibatch_size=32, graph_filename='costs'):
"""
Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.
Input:
train : training set
test : test set
learning_rate : learning rate
num_epochs : number of epochs
minibatch_size : size of minibatch
print_cost : True to print the cost every epoch
Returns:
parameters : parameters learnt by the model
"""
ops.reset_default_graph() #for rerunning the model without resetting tf vars
# input and output shapes
(n_x, m) = train.images.T.shape
n_y = train.labels.T.shape[0]
costs = [] #var for storing the costs for later use
# create placeholders
X, Y = placeholderCreator(n_x, n_y)
parameters = paramInitializer()
# Forward propagation
Z3 = forwardPropagation(X, parameters)
# Cost function
cost = costCalc(Z3, Y)
#Backpropagation using adam optimizer
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
# Initialize tf variables
init = tf.global_variables_initializer()
minibatch_size = 32
# Start session to compute Tensorflow graph
with tf.Session() as sess:
# Run initialization
sess.run(init)
for epoch in range(num_epochs): # Training loop
epoch_cost = 0.
num_minibatches = int(m / minibatch_size)
for i in range(num_minibatches):
minibatch_X, minibatch_Y = train.next_batch(minibatch_size) # Get next batch of training data and labels
_, minibatch_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X.T, Y: minibatch_Y.T}) # Execute optimizer and cost function
epoch_cost += minibatch_cost / num_minibatches # Update epoch cost
saver = tf.train.Saver()
# Save parameters
parameters = sess.run(parameters)
saver.save(sess, "~/trained-model.ckpt")
return parameters
And this is my predict function where I am trying to predict an image. I have converted that image into MNIST format for ease of use (predicting_data). I load the model that I saved, use a softmax function on the output of 3rd layer (final output).
def predict():
train = predicting_data.train
(n_x, m) = train.images.T.shape
n_y = train.labels.T.shape[0]
X, Y = placeholderCreator(n_x, n_y)
with tf.Session() as sess:
new_saver = tf.train.import_meta_graph('~/trained-model.ckpt.meta')
new_saver.restore(sess, '~/trained-model.ckpt')
W1 = tf.get_default_graph().get_tensor_by_name('W1:0')
b1 = tf.get_default_graph().get_tensor_by_name('b1:0')
W2 = tf.get_default_graph().get_tensor_by_name('W2:0')
b2 = tf.get_default_graph().get_tensor_by_name('b2:0')
W3 = tf.get_default_graph().get_tensor_by_name('W3:0')
b3 = tf.get_default_graph().get_tensor_by_name('b3:0')
# forward propagation
Z1 = tf.add(tf.matmul(W1,X), b1)
A1 = tf.nn.relu(Z1)
Z2 = tf.add(tf.matmul(W2,A1), b2)
A2 = tf.nn.relu(Z2)
Z3 = tf.add(tf.matmul(W3,A2), b3)
y_pred = tf.nn.softmax(Z3) ####what to do after this????
cost = sess.run(y_pred, feed_dict={X: train.images.T})
Thank you in advance!

Answer 1

As vijay says in his comment:
Your predict part is not right, you need to get the input and predict tensors from the saved graph using the get_tensor_by_name() function and then use it in your sess.run
If you look at this post, it covers a similar problem and has some code examples.

Answer 2

In your code, you can pass 1 to the next_batch method and get just one image.
minibatch_X, minibatch_Y = train.next_batch(1)