Separating graph definition from session

examples/03_logistic_regression_mnist_sol.py

@@ -1,4 +1,4 @@
-""" Simple logistic regression model to solve OCR task 
+""" Simple logistic regression model to solve OCR task
 with MNIST in TensorFlow
 MNIST dataset: yann.lecun.com/exdb/mnist/
 Author: Chip Huyen
@@ -20,14 +20,14 @@
 
 # Step 1: Read in data
 # using TF Learn's built in function to load MNIST data to the folder data/mnist
-mnist = input_data.read_data_sets('/data/mnist', one_hot=True) 
+mnist = input_data.read_data_sets('./data/mnist', one_hot=True)
 
 # Step 2: create placeholders for features and labels
 # each image in the MNIST data is of shape 28*28 = 784
 # therefore, each image is represented with a 1x784 tensor
-# there are 10 classes for each image, corresponding to digits 0 - 9. 
+# there are 10 classes for each image, corresponding to digits 0 - 9.
 # each lable is one hot vector.
-X = tf.placeholder(tf.float32, [batch_size, 784], name='X_placeholder') 
+X = tf.placeholder(tf.float32, [batch_size, 784], name='X_placeholder')
 Y = tf.placeholder(tf.int32, [batch_size, 10], name='Y_placeholder')
 
 # Step 3: create weights and bias
@@ -41,7 +41,7 @@
 # Step 4: build model
 # the model that returns the logits.
 # this logits will be later passed through softmax layer
-logits = tf.matmul(X, w) + b 
+logits = tf.matmul(X, w) + b
 
 # Step 5: define loss function
 # use cross entropy of softmax of logits as the loss function
@@ -52,19 +52,24 @@
 # using gradient descent with learning rate of 0.01 to minimize loss
 optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)
 
+# Step 7: Adding testing part to graph to calculate accuracy
+preds = tf.nn.softmax(logits)
+correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(Y, 1))
+accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32)) # need numpy.count_nonzero(boolarr) :(
+
 with tf.Session() as sess:
 	# to visualize using TensorBoard
 	writer = tf.summary.FileWriter('./graphs/logistic_reg', sess.graph)
 
 	start_time = time.time()
-	sess.run(tf.global_variables_initializer())	
+	sess.run(tf.global_variables_initializer())
 	n_batches = int(mnist.train.num_examples/batch_size)
 	for i in range(n_epochs): # train the model n_epochs times
 		total_loss = 0
 
 		for _ in range(n_batches):
 			X_batch, Y_batch = mnist.train.next_batch(batch_size)
-			_, loss_batch = sess.run([optimizer, loss], feed_dict={X: X_batch, Y:Y_batch}) 
+			_, loss_batch = sess.run([optimizer, loss], feed_dict={X: X_batch, Y:Y_batch})
 			total_loss += loss_batch
 		print('Average loss epoch {0}: {1}'.format(i, total_loss/n_batches))
 
@@ -73,19 +78,15 @@
 	print('Optimization Finished!') # should be around 0.35 after 25 epochs
 
 	# test the model
-
-	preds = tf.nn.softmax(logits)
-	correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(Y, 1))
-	accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32)) # need numpy.count_nonzero(boolarr) :(
-
+
 	n_batches = int(mnist.test.num_examples/batch_size)
 	total_correct_preds = 0
-	
+
 	for i in range(n_batches):
 		X_batch, Y_batch = mnist.test.next_batch(batch_size)
-		accuracy_batch = sess.run([accuracy], feed_dict={X: X_batch, Y:Y_batch}) 
-		total_correct_preds += accuracy_batch	
-	
+		accuracy_batch = sess.run(accuracy, feed_dict={X: X_batch, Y:Y_batch})
+		total_correct_preds += accuracy_batch
+
 	print('Accuracy {0}'.format(total_correct_preds/mnist.test.num_examples))
 
 	writer.close()