12、卷积与循环神经网络：从理论到实践

卷积与循环神经网络：从理论到实践

1. 卷积神经网络训练

在TensorFlow中训练卷积神经网络，首先要定义占位符，为网络架构做好准备。以下是示例代码及详细解释：

BATCH_SIZE = 64
EVAL_BATCH_SIZE = 64
train_data_node = tf.placeholder(
    tf.float32,
    shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,))
eval_data = tf.placeholder(
    tf.float32,
    shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))

上述代码定义了训练数据、训练标签和评估数据的占位符。训练数据和评估数据的占位符形状包含了批量大小、图像尺寸和通道数等信息。

接下来是训练LeNet - 5架构的代码：

# Create a local session to run the training.
start_time = time.time()
with tf.Session() as sess:
  # Run all the initializers to prepare the trainable parameters.
  tf