Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- import tensorflow as tf
- image_size = 224
- 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")
- # Fourth CONV layer
- kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 384],
- dtype=tf.float32,
- stddev=1e-1),
- name="conv4_weights")
- conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding="SAME")
- bias = tf.Variable(tf.truncated_normal([384]), name="conv4_bias")
- conv_with_bias = tf.nn.bias_add(conv, bias)
- conv4 = tf.nn.relu(conv_with_bias, name="conv4")
- # Fifth CONV Layer
- kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 256],
- dtype=tf.float32,
- stddev=1e-1),
- name="conv5_weights")
- conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding="SAME")
- bias = tf.Variable(tf.truncated_normal([256]), name="conv5_bias")
- conv_with_bias = tf.nn.bias_add(conv, bias)
- conv5 = tf.nn.relu(conv_with_bias, name="conv5")
Add Comment
Please, Sign In to add comment