Created
January 20, 2018 05:39
-
-
Save anyuzx/28c62650d13703ecd0d6aa3ae43132c5 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
anisotropy = False | |
learning_rate = 0.005 | |
batch_size = 200 | |
h = 10 | |
w = 10 | |
channels = 1 | |
x = tf.placeholder(tf.float32, [batch_size, h, w, channels]) | |
y = tf.placeholder(tf.float32, [batch_size, h, w, channels]) | |
linear_map = np.random.rand(h,w) | |
hidden_size = 100 | |
rnn_out, _ = multi_dimensional_rnn_while_loop(rnn_size=hidden_size, input_data=x, sh=[1, 1]) | |
# use linear activation function | |
model_out = slim.fully_connected(inputs=rnn_out, | |
num_outputs=1, | |
activation_fn=None) | |
# use a little different loss function from the original code | |
loss = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(y, model_out)))) | |
grad_update = tf.train.AdamOptimizer(learning_rate).minimize(loss) | |
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False)) | |
sess.run(tf.global_variables_initializer()) | |
# Add tensorboard (Really usefull) | |
train_writer = tf.summary.FileWriter('Tensorboard_out' + '/MDLSTM',sess.graph) | |
steps = 1000 | |
mypredict_result = [] | |
loss_series = [] | |
for i in range(steps): | |
batch = next_batch_linear_map(batch_size, h, w, linear_map, anisotropy) | |
st = time() | |
batch_x = np.expand_dims(batch[0], axis=3) | |
batch_y = np.expand_dims(batch[1], axis=3) | |
mypredict, loss_val, _ = sess.run([model_out, loss, grad_update], feed_dict={x: batch_x, y: batch_y}) | |
mypredict_result.append([batch_x, batch_y, mypredict]) | |
print('steps = {0} | loss = {1:.3f} | time {2:.3f}'.format(str(i).zfill(3), | |
loss_val, | |
time() - st)) | |
loss_series.append([i+1, loss_val]) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment