(network, X_batch, y_batch, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None)
| 49 | |
| 50 | |
| 51 | def _train_step(network, X_batch, y_batch, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None): |
| 52 | with tf.GradientTape() as tape: |
| 53 | y_pred = network(X_batch) |
| 54 | _loss = cost(y_pred, y_batch) |
| 55 | grad = tape.gradient(_loss, network.trainable_weights) |
| 56 | train_op.apply_gradients(zip(grad, network.trainable_weights)) |
| 57 | if acc is not None: |
| 58 | _acc = acc(y_pred, y_batch) |
| 59 | return _loss, _acc |
| 60 | else: |
| 61 | return _loss, None |
| 62 | |
| 63 | |
| 64 | def accuracy(_logits, y_batch): |
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