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)
