这种行为通过设置training=False(或者通过使用tf.keras.Model.predict方法)来实现。 这种差异是为了在推理时使用更稳定的统计量,因为单个批次的数据可能会有较大的波动,而滑动平均能更好地代表整个数据集的分布。
optimizer.apply_gradients(zip(gradients, model.trainable_variables)) train_loss(loss) train_accuracy(labels, predictions) @tf.function def test_step(images, labels): # training=False is only needed if there are layers with different # behavior during training versus inference (e.g. Dropout). pr...
问题的关键还是在于Keras+TensorFlow2.0里面我们如何处理在training和testing状态下行为不一致的Layer;以及对于model.fit()和tf.funtion这两种训练方法的区别,最终来看model.fit()里面似乎包含很多诡异的行为。
Theapply()method adds shadow copies of trained variables and add ops that maintain a moving average of the trained variables in their shadow copies. It is used when building the training model. The ops that maintain moving averages are typically run after each training step. Theaverage()andave...
>>> training_finished = False >>> class MyCallback(tf.keras.callbacks.Callback): ... def on_train_end(self, logs=None): ... global training_finished ... training_finished = True >>> model = tf.keras.Sequential([tf.keras.layers.Dense(1, input_shape=(1,))]) ...
model.optimizer.lr.assign(model.optimizer.lr/2.0) tf.print("Lowering optimizer Learning Rate...\n\n")forx, yinds_train: train_result=model.train_on_batch(x, y)forx, yinds_valid: valid_result= model.test_on_batch(x, y,reset_metrics=False)ifepoch%1 ==0: ...
train_on_batch(x, y) for x, y in ds_valid: valid_result = model.test_on_batch(x, y,reset_metrics=False) if epoch%1 ==0: printbar() tf.print("epoch = ",epoch) print("train:",dict(zip(model.metrics_names,train_result))) print("valid:",dict(zip(model.metrics_names,valid_...
with tf.Graph().as_default(): print(tf.executing_eagerly()) # False model = Model(num_actions=env.action_space.n) agent = A2CAgent(model) rewards_history = agent.train(env) print("Finished training, testing...") print("%d out of 200" % agent.test(env)) # 200 out of 200 ...
y = model(tf.ones((2, 16)), training=True) 这就是函数API,它比子分类更简洁易用,不过它只能用于定义层中的DAG。 掌握了上述指南12条,就能实现大多数深度学习研究了,是不是赞赞的。 传送门 最后,附Chollet推特原文地址: https://twitter.com/fchollet/status/1105139360226140160 ...
# Toggle between training from model and from real environment allowing sufficient time # to train the model before its used for learning policy if num_episode > train_first_steps: train_from_model = not train_from_model # If batch full ...