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import numpy as np
import tensorflow as tf
from tensorflow.python.framework import dtypes
# 1. Setting up initial values
x = np.zeros((7, 7, 3))
x[:, :, 0] = np.mat(
"0 0 0 0 0 0 0;0 0 1 0 1 0 0;0 2 1 0 1 2 0;0 0 2 0 0 1 0;0 2 0 1 0 0 0;0 0 0 1 2 2 0;0 0 0 0 0 0 0"
).A
x[:, :, 1] = np.mat(
"0 0 0 0 0 0 0;0 1 0 0 1 1 0;0 0 2 2 1 1 0;0 2 1 2 1 0 0;0 2 1 1 2 2 0;0 1 2 0 2 2 0;0 0 0 0 0 0 0"
).A
x[:, :, 2] = np.mat(
"0 0 0 0 0 0 0;0 2 1 1 1 1 0;0 2 2 1 2 1 0;0 1 1 0 2 2 0;0 2 1 2 2 0 0;0 1 2 2 0 0 0;0 0 0 0 0 0 0"
).A
x = np.reshape(x, (1, 7, 7, 3))
# print("x:",x)
w = np.zeros((3, 3, 3, 2))
w[:, :, 0, 0] = np.mat("0 0 1;-1 1 1;0 1 0").A
w[:, :, 1, 0] = np.mat("1 1 1;0 1 1;0 1 0").A
w[:, :, 2, 0] = np.mat("-1 0 0;-1 1 1;0 -1 0").A
# w1 = np.zeros((3,3,3))
w[:, :, 0, 1] = np.mat("0 0 0;1 1 -1;-1 1 1").A
w[:, :, 1, 1] = np.mat("0 1 -1;1 1 -1;-1 1 -1").A
w[:, :, 2, 1] = np.mat("1 1 0;-1 -1 0;0 -1 1").A
stride = 2
scope = "conv_in_numpy"
act = tf.nn.relu # activation
pad = 'VALID' # padding
nf = 2 # number of filters
rf = 3 # filter size
b = [1, 0] # bias
np_o = np.zeros((1, 3, 3, 2))
s = stride
# 2. CNN in Tensorflow
print("--- Convolution in Tensorflow ---")
tf_x = tf.constant(x, dtype=dtypes.float32)
with tf.Session() as sess:
with tf.variable_scope(scope):
nin = tf_x.get_shape()[3].value
tf_w = tf.get_variable("w", [rf, rf, nin, nf], initializer=tf.constant_initializer(w))
tf_b = tf.get_variable(
"b", [nf],
initializer=tf.constant_initializer(b, dtype=dtypes.float32))
tf_z = tf.nn.conv2d(
tf_x, w, strides=[1, stride, stride, 1], padding=pad) + b
tf_h = act(tf_z)
sess.run(tf.global_variables_initializer())
tf_o = sess.run(tf_z)
print("tf_o0:\n", tf_o[0, :, :, 0])
print("tf_o1:\n", tf_o[0, :, :, 1])
# 3. CNN in numpy
print("--- Convolution in numpy ---")
for z in range(nf):
print("z:", z)
h_range = int((x.shape[2] - rf) / s) + 1 # (W - F + 2P) / S
for _h in range(h_range):
w_range = int((x.shape[1] - rf) / s) + 1 # (W - F + 2P) / S
for _w in range(w_range):
np_o[0, _h, _w, z] = np.sum(
x[0, _h * s:_h * s + rf, _w * s:_w * s + rf, :] *
w[:, :, :, z]) + b[z]
print("np_o0:\n", np_o[0, :, :, 0])
print("np_o1:\n", np_o[0, :, :, 1])
np.testing.assert_almost_equal(tf_o, np_o)