Keras连接的卷积层:每个Conv层上需要的数据格式

使用已连接的连续 Conv2D Keras中的图层,是否需要设置 data_format 每一层,还是第一层?我的数据是NCHW(频道优先)格式。

为了提供一些上下文,我有一个由多个连接的 Conv2D 层层叠叠。我的图像是:

• 灰度;
• 84x84像素;
• 堆叠4个,以便每个样本都有速度指示(即,一个样本由4个序列图像组成,我对这些样本的批次进行训练)。

换句话说,每个样品的形状 (4, 84, 84) .这是我的模型,它是一个Deep-q网络实现:

import numpy as np
import tensorflow as tf

'''
 ' Huber loss: https://en.wikipedia.org/wiki/Huber_loss
'''
def huber_loss(y_true, y_pred):
  error = y_true - y_pred
  cond  = tf.keras.backend.abs(error) < 1.0

  squared_loss = 0.5 * tf.keras.backend.square(error)
  linear_loss  = tf.keras.backend.abs(error) - 0.5

  return tf.where(cond, squared_loss, linear_loss)

'''
 ' Importance Sampling weighted huber loss.
'''
def huber_loss_mean_weighted(y_true, y_pred, is_weights):
  error = huber_loss(y_true, y_pred)

  return tf.keras.backend.mean(error * is_weights)


# The observation input.
in_obs = tf.keras.layers.Input(shape=(4, 84, 84))

# The importance sampling weights are used with the custom loss function,
# and correct for the non-uniform distribution of the samples.
in_is_weights = tf.keras.layers.Input(shape=(1,))

# Expectations when training (the output is qualities for actions).
in_actual = tf.keras.layers.Input(shape=(4,))

# Normalize the observation to the range of [0, 1].
norm = tf.keras.layers.Lambda(lambda x: x / 255.0)(in_obs)

# Convolutional layers per the Nature paper on DQN.
conv1 = tf.keras.layers.Conv2D(filters=32, kernel_size=8, strides=4,
  activation="relu", data_format="channels_first")(norm)
conv2 = tf.keras.layers.Conv2D(filters=64, kernel_size=4, strides=2,
  activation="relu", data_format="channels_first")(conv1)
conv3 = tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=1,
  activation="relu", data_format="channels_first")(conv2)

# Flatten, and move to the fully-connected part of the network.
flatten = tf.keras.layers.Flatten()(conv3)
dense1  = tf.keras.layers.Dense(512, activation="relu")(flatten)

# Output prediction.
out_pred = tf.keras.layers.Dense(4, activation="linear")(dense1)

# Using Adam optimizer, RMSProp's successor.
opt = tf.keras.optimizers.Adam(lr=5e-5, decay=0.0)

# This network is used for training.
train_network = tf.keras.models.Model(
  inputs=[in_obs, in_actual, in_is_weights],
  outputs=out_pred)

# The custom loss, which is Huber Loss weighted by IS weights.
train_network.add_loss(
  huber_loss_mean_weighted(out_pred, in_actual, in_is_weights))

train_network.compile(optimizer=opt, loss=None)

提前感谢您的帮助。