Skip to content

Update main.py #58

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Update main.py #58

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
19 changes: 3 additions & 16 deletions 03-Play-with-MNIST/main.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,7 +1,5 @@
import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics


from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics


(xs, ys),_ = datasets.mnist.load_data()
Expand Down Expand Up @@ -30,26 +28,15 @@
x = tf.reshape(x, (-1, 28*28))
# [b, 784] => [b, 10]
out = network(x)
# [b] => [b, 10]
y_onehot = tf.one_hot(y, depth=10)
# [b, 10]
loss = tf.square(out-y_onehot)
# [b]
loss = tf.square(out-y) # The shape of y is (batch_size, 10), so you don't need to encode it any more.
loss = tf.reduce_sum(loss) / 32


acc_meter.update_state(tf.argmax(out, axis=1), y)
acc_meter.update_state(tf.argmax(out, axis=1), tf.argmax(y, axis=1))

grads = tape.gradient(loss, network.trainable_variables)
optimizer.apply_gradients(zip(grads, network.trainable_variables))


if step % 200==0:

print(step, 'loss:', float(loss), 'acc:', acc_meter.result().numpy())
acc_meter.reset_states()