optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) #optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) rmse = tf.sqrt(tf.reduce_mean(tf.squared_difference(out, y_))) # Initialize init = tf.initialize_all_variables() dh = DataHelper(batch_size, test_idx=test_start) saver = tf.train.Saver() with tf.Session() as sess: sess.run(init) print sess.run(W_conv1), sess.run(b_conv1), sess.run(W_conv2), sess.run( b_conv2) test_data, test_labels = dh.get_test_data(test_size) epoch = 1 train_start = time.time() while epoch <= epochs: epoch_start = time.time() print 'Training Epoch {}...'.format(epoch) # get data, test_idx = 19000 is ~83% train test split dh = DataHelper(batch_size, test_idx=test_start) # test data step = 1 # Looks like training iters in the number of images to process while step * batch_size < test_start: # TODO get data in proper format batch_xs, batch_ys = dh.get_next_batch() #print batch_xs.shape, batch_ys.shape #sys.exit(0)