(shape)
| 26 | return tf.Variable(initial) |
| 27 | |
| 28 | def bias_variable(shape): |
| 29 | initial = tf.constant(0.01, shape = shape) |
| 30 | return tf.Variable(initial) |
| 31 | |
| 32 | def conv2d(x, W, stride): |
| 33 | return tf.nn.conv2d(x, W, strides = [1, stride, stride, 1], padding = "SAME") |