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- ```python
- import numpy as np
- import tensorflow as tf
- # np.set_printoptions(threshold=np.nan)
- def run(x, w, pad='SAME', stride=(1, 1)):
- xx = tf.constant(x, dtype='float32')
- ww = tf.constant(w, dtype='float32')
- yy = tf.nn.conv2d(xx, ww, strides=[1, stride[0], stride[1], 1], padding=pad)
- with tf.Session() as sess:
- out = yy.eval().ravel()
- return out
- if __name__ == '__main__':
- np.random.seed(0)
- # input [batch, in_height, in_width, in_channels]
- x = np.random.rand(1, 224, 224, 3).astype('float32')
- # filter [filter_height, filter_width, in_channels, out_channels]
- w = np.random.rand(3, 3, 3, 32).astype('float32')
- out = run(x, w)
- print(out.shape)
- print(out)
- ```
- ```
- $ python validate-conv2d.py
- (1605632,)
- [3.6909204 3.6372736 3.3944104 ... 3.8938167 2.8258123 4.4820447]
- ```
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