Question

我正在尝试将TensorFlow API中编写的一些开源代码转换为Keras API。我正在为每一层获取正确的输入和输出形状，但是，当我尝试对模型本身进行测试时，它会抱怨。请原谅代码，为简洁起见，我简化了很多。

这是TensorFlow API：

        hidden_units_g = 100
        num_generated_features = 1
        seq_length = 30
        batch_size = 28
        latent_dim = 5

        W_out_G = tf.get_variable(name='W_out_G', shape=[hidden_units_g, num_generated_features], initializer=W_out_G_initializer)
        b_out_G = tf.get_variable(name='b_out_G', shape=num_generated_features, initializer=b_out_G_initializer)

        # shape: (28, 30, 5)
        inputs = tf.random_normal(shape=(batch_size, seq_length, latent_dim))

        cell = LSTMCell(num_units=hidden_units_g,
                           state_is_tuple=True,
                           initializer=lstm_initializer,
                           bias_start=bias_start,
                           reuse=reuse)

        # rnn_output shape: (28, 30, 100)
        rnn_outputs, rnn_states = tf.nn.dynamic_rnn(
            cell=cell,
            dtype=tf.float32,
            sequence_length=[seq_length]*batch_size,
            inputs=inputs)

        # reshape shape: (840, 100)
        rnn_outputs_2d = tf.reshape(rnn_outputs, [-1, hidden_units_g])

        # shape: ( 840, 1)
        logits_2d = tf.matmul(rnn_outputs_2d, W_out_G) + b_out_G

        # shape: (840, 1)
        output_2d = tf.nn.tanh(logits_2d)

        # shape: (28, 30, 1)
        output_3d = tf.reshape(output_2d, [-1, seq_length, num_generated_features])

这是我在Keras中重新创建的尝试：

gen = tf.keras.models.Sequential([
    tf.keras.layers.LSTM(100, input_shape=(30, 5), return_sequences=False),
    #tf.keras.layers.Dense(1, activation='tanh'),
    tf.keras.layers.Dense(1, activation='tanh'),
    tf.keras.layers.Reshape(target_shape=(30, 1))
])
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm (LSTM)                  (None, 100)               42400     
_________________________________________________________________
dense (Dense)                (None, 1)                 101       
_________________________________________________________________
reshape (Reshape)            (None, 30, 1)             0         
=================================================================
Total params: 42,501
Trainable params: 42,501
Non-trainable params: 0

还应用tf.keras.utils.plot_model()确认我的输入/输出形状完全相同。但是，当我尝试使用np.random.randn(28, 30, 5)进行模拟测试时，我收到一条错误消息，提示它无法重塑密集输出，它期望840个值，但只能看到28个。

在Keras中移动模型时，是否可以打印出实际的批生产形状？

谢谢

编辑：这是使用keras.utils.plot_model()

时的完整输入/输出

LSTM: input: (?, 30, 5)
      output:(?, 100)

Dense: Input(?, 100)
     : Output: (?, 1)

Reshape: Input: (?, 1)
         Output: (?, 30, 1)

无法将用Tensorflow API编写的LSTM模型转换为Keras API

0 个答案: