Making predictions with a TensorFlow model

In the "Deep MNIST for Experts" example, see this line:

We can now implement our regression model. It only takes one line! We multiply the vectorized input images x by the weight matrix W, add the bias b, and compute the softmax probabilities that are assigned to each class.

y = tf.nn.softmax(tf.matmul(x,W) + b)

Just pull on node y and you'll have what you want.

feed_dict = {x: [your_image]}
classification = tf.run(y, feed_dict)
print classification

This applies to just about any model you create - you'll have computed the prediction probabilities as one of the last steps before computing the loss.

As @dga suggested, you need to run your new instance of the data though your already predicted model.

Here is an example:

Assume you went though the first tutorial and calculated the accuracy of your model (the model is this: y = tf.nn.softmax(tf.matmul(x, W) + b)). Now you grab your model and apply the new data point to it. In the following code I calculate the vector, getting the position of the maximum value. Show the image and print that maximum position.

from matplotlib import pyplot as plt
from random import randint
num = randint(0, mnist.test.images.shape[0])
img = mnist.test.images[num]

classification = sess.run(tf.argmax(y, 1), feed_dict={x: [img]})
plt.imshow(img.reshape(28, 28), cmap=plt.cm.binary)
plt.show()
print 'NN predicted', classification[0]