Question

class Model:
    def __init__(
        self,
        learning_rate,
        num_layers,
        size,
        size_layer,
        output_size,
        forget_bias = 0.1,
    ):
        def lstm_cell(size_layer):
            return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)

        rnn_cells = tf.compat.v1.nn.rnn_cell.MultiRNNCell(
            [lstm_cell(size_layer) for _ in range(num_layers)],
            state_is_tuple = False,
        )
        self.X = tf.compat.v1.placeholder(tf.float32, (None, None, size))
        self.Y = tf.compat.v1.placeholder(tf.float32, (None, output_size))
        drop = tf.compat.v1.nn.rnn_cell.DropoutWrapper(
            rnn_cells, output_keep_prob = forget_bias
        )
        self.hidden_layer = tf.compat.v1.placeholder(
            tf.float32, (None, num_layers * 2 * size_layer)
        )
        self.outputs, self.last_state = tf.compat.v1.nn.dynamic_rnn(
            drop, self.X, initial_state = self.hidden_layer, dtype = tf.float32
        )
        self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)
        self.cost = tf.reduce_mean(tf.square(self.Y - self.logits))
        self.optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate).minimize(
            self.cost
        )

我想将上面的代码转换为相关的TensorFlow 2.x，而又不急于执行，有人可以帮忙吗？我一直在尝试更改一些内容，例如：将tf.compat.v1.nn.rnn_cell.LSTMCell更改为tf.keras.layers.LSTMCell，将tf.compat.v1.nn.rnn_cell.MultiRNNCell更改为tf.keras.layers.StackedRNNCells，也将tf.compat.v1.nn.dynamic_rnn更改为tf.keras.layers.RNN 我该怎么办？

Answer 1

TensorFlow 1.x脚本无法直接与TensorFlow 2.x一起使用，但需要进行转换。

tf_upgrade_v2 --infile tensorflow_v1.py --outfile tensorflow_v2.py

如果一个文件夹中有多个文件，则可以使用以下命令。

tf_upgrade_v2  v1-code-folder  code-upgraded-folder

如何从TensorFlow 1.x迁移到TensorFlow 2.x

1 个答案: