CODEWITHSUNDEEP
pythonmachine-learningtensorflow
buydadip
buydadip

Reputation: 9407

How to use a trained model on different inputs

I implemented a relatively straightforward logistic regression function. I save all the necessary variables such as weights, bias, x, y, etc. and then I run the training algorithm...

# launch the graph
with tf.Session() as sess:

    sess.run(init)

    # training cycle
    for epoch in range(FLAGS.training_epochs):
        avg_cost = 0
        total_batch = int(mnist.train.num_examples/FLAGS.batch_size)
        # loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size)

            _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, y: batch_ys})

            # compute average loss
            avg_cost += c / total_batch
        # display logs per epoch step
        if (epoch + 1) % FLAGS.display_step == 0:
            print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))

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

The model is saved and the prediction and accuracy of the trained model is displayed...

# list of booleans to determine the correct predictions
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
print(correct_prediction.eval({x:mnist.test.images, y:mnist.test.labels}))

# calculate total accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

This is all fine and dandy. However, now I want to be able to predict any given image using the trained model. For example, I want to feed it picture of say 7 and see what it predicts it to be.

I have another module that restores the model. First we load the variables...

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

# tf Graph Input
x = tf.placeholder(tf.float32, [None, 784])  # mnist data image of shape 28*28=784
y = tf.placeholder(tf.float32, [None, 10])  # 0-9 digits recognition => 10 classes

# set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

# construct model
pred = tf.nn.softmax(tf.matmul(x, W) + b)  # Softmax

# minimize error using cross entropy
cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1))
# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(cost)

# initializing the variables
init = tf.global_variables_initializer()

saver = tf.train.Saver()

with tf.Session() as sess:
    save.restore(sess, "/tmp/model.ckpt")

This is good. Now I want to compare one image to the model and get a prediction. In this example, I take the first image from the test dataset mnist.test.images[0] and I attempt to compare it to the model.

classification = sess.run(tf.argmax(pred, 1), feed_dict={x: mnist.test.images[0]})
print(classification)

I know this will not work. I get the error...

ValueError: Cannot feed value of shape (784,) for Tensor 'Placeholder:0', which has shape '(?, 784)'

I am at a loss for ideas. This question is rather long, if a straightforward answer is not possible, some guidance as to the steps I may take to do this is appreciated.

Upvotes: 1

Views: 484

Answers (1)

kafman
kafman

Reputation: 2860

Your input placeholder must be of size (?, 784), the question mark meaning variable size which is probably the batch size. You are feeding an input of size (784,) which does not work as the error message states.

In your case, during prediction time, the batch size is just 1, so the following should work:

import numpy as np
...
x_in = np.expand_dims(mnist.test.images[0], axis=0)
classification = sess.run(tf.argmax(pred, 1), feed_dict={x:x_in})

Assuming that the input image is available as a numpy array. If it is already a tensor, the corresponding function is tf.expand_dims(..).

Upvotes: 1

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