Created
November 28, 2019 10:29
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demo_ll.py
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| @tf.function | |
| def model(x, w0, b0, w1, b1, ...): | |
| x = tf.nn.sigmoid(tf.matmul(x, w0) + b0) | |
| x = tf.nn.sigmoid(tf.matmul(x, w1) + b1) | |
| return x | |
| def train(...): | |
| for (x,y) in train_ds: | |
| with tf.GradientTape() as tape: | |
| loss = compute_loss(model(x, ...), y) | |
| grads = tape.gradient(loss, model.trainable_variables) | |
| optimizer.apply_gradients(zip(grads, model.trainable_variables)) | |
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