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目录
import tensorflow as tf
from tensorflow.keras import layers
x = tf.random.normal([1, 32, 32, 3])
# padding='valid':输入和输出维度不同
layer = layers.Conv2D(4, kernel_size=5, strides=1, padding='valid')
out = layer(x)
out.shape
TensorShape([1, 28, 28, 4])
# padding='same':输入和输出维度相同
layer = layers.Conv2D(4, kernel_size=5, strides=1, padding='same')
out = layer(x)
out.shape
TensorShape([1, 32, 32, 4])
layer = layers.Conv2D(4, kernel_size=5, strides=2, padding='same')
out = layer(x)
out.shape
TensorShape([1, 16, 16, 4])
layer.call(x).shape
TensorShape([1, 16, 16, 4])
layer = layers.Conv2D(4, kernel_size=5, strides=2, padding='same')
out = layer(x)
out.shape
TensorShape([1, 16, 16, 4])
# 5,5--》size,3--》通道数,4--》核数量
layer.kernel.shape
TensorShape([5, 5, 3, 4])
layer.bias
<tf.Variable 'conv2d_11/bias:0' shape=(4,) dtype=float32, numpy=array([0., 0., 0., 0.], dtype=float32)>
w = tf.random.normal([5, 5, 3, 4])
b = tf.zeros([4])
x.shape
TensorShape([1, 32, 32, 3])
out = tf.nn.conv2d(x, w, strides=1, padding='VALID')
out.shape
TensorShape([1, 28, 28, 4])
out = out + b
out.shape
TensorShape([1, 28, 28, 4])
out = tf.nn.conv2d(x, w, strides=2, padding='VALID')
out.shape
TensorShape([1, 14, 14, 4])
[ frac{partial{Loss}}{partial{w}} ]
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