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@joeyism
Last active August 8, 2018 01:17
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import tensorflow as tf
image_size = 32
input_images = tf.placeholder(tf.float32,
shape=[None, image_size, image_size, 3],
name="input_images")
# First CONV layer
kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 96],
dtype=tf.float32,
stddev=1e-1),
name="conv1_weights")
conv = tf.nn.conv2d(input_images, kernel, [1, 4, 4, 1], padding="SAME")
bias = tf.Variable(tf.truncated_normal([96]))
conv_with_bias = tf.nn.bias_add(conv, bias)
conv1 = tf.nn.relu(conv_with_bias, name="conv1")
lrn1 = tf.nn.lrn(conv1,
alpha=1e-4,
beta=0.75,
depth_radius=2,
bias=2.0)
pooled_conv1 = tf.nn.max_pool(lrn1,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding="SAME",
name="pool1")
# Second CONV Layer
kernel = tf.Variable(tf.truncated_normal([5, 5, 96, 256],
dtype=tf.float32,
stddev=1e-1),
name="conv2_weights")
conv = tf.nn.conv2d(pooled_conv1, kernel, [1, 4, 4, 1], padding="SAME")
bias = tf.Variable(tf.truncated_normal([256]), name="conv2_bias")
conv_with_bias = tf.nn.bias_add(conv, bias)
conv2 = tf.nn.relu(conv_with_bias, name="conv2")
lrn2 = tf.nn.lrn(conv2,
alpha=1e-4,
beta=0.75,
depth_radius=2,
bias=2.0)
pooled_conv2 = tf.nn.max_pool(lrn2,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding="SAME",
name="pool2")
# Third CONV layer
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 384],
dtype=tf.float32,
stddev=1e-1),
name="conv3_weights")
conv = tf.nn.conv2d(pooled_conv2, kernel, [1, 1, 1, 1], padding="SAME")
bias = tf.Variable(tf.truncated_normal([384]), name="conv3_bias")
conv_with_bias = tf.nn.bias_add(conv, bias)
conv3 = tf.nn.relu(conv_with_bias, name="conv3")
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