Question

TensorFlow图API分离了图的构造和执行。因此，我无法理解在哪条线神经网络中执行。

"""
- model_fn: function that performs the forward pass of the model
- init_fn: function that initializes the parameters of the model.
- learning_rate: the learning rate to use for SGD.
"""
tf.reset_default_graph()
is_training = tf.placeholder(tf.bool, name='is_training')

with tf.device(device):
    x = tf.placeholder(tf.float32, [None, 32, 32, 3])
    y = tf.placeholder(tf.int32, [None])
    params = init_fn()           # Initialize the model parameters
    scores = model_fn(x, params) # Forward pass of the model
    loss = training_step(scores, y, params, learning_rate) # SGD

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for t, (x_np, y_np) in enumerate(train_dset):

        feed_dict = {x: x_np, y: y_np}
        loss_np = sess.run(loss, feed_dict=feed_dict)

Answer 1

如Tensorflow文档中所述：（https://www.tensorflow.org/api_docs/python/tf/Session#run）

通过运行以下方法，该方法运行TensorFlow计算的一个“步骤” 执行每个操作并评估每个操作所需的图形片段张量

在您的示例中，sess.run(tf.global_variables_initializer())运行创建所有权重和张量的初始化操作，loss_np = sess.run(loss, feed_dict=feed_dict)执行直至loss的所有操作。

我希望这能回答您的问题

在张量流中执行神经网络意味着什么

1 个答案: