add SqueezeNet

Author: xiaohu2015

CNNs/SqueezeNet.py

@@ -0,0 +1,72 @@
+"""
+2017/12/02
+"""
+
+import tensorflow as tf
+import numpy as np
+
+
+class SqueezeNet(object):
+    def __init__(self, inputs, nb_classes=1000, is_training=True):
+        # conv1
+        net = tf.layers.conv2d(inputs, 96, [7, 7], strides=[2, 2],
+                                 padding="SAME", activation=tf.nn.relu,
+                                 name="conv1")
+        # maxpool1
+        net = tf.layers.max_pooling2d(net, [3, 3], strides=[2, 2],
+                                      name="maxpool1")
+        # fire2
+        net = self._fire(net, 16, 64, "fire2")
+        # fire3
+        net = self._fire(net, 16, 64, "fire3")
+        # fire4
+        net = self._fire(net, 32, 128, "fire4")
+        # maxpool4
+        net = tf.layers.max_pooling2d(net, [3, 3], strides=[2, 2],
+                                      name="maxpool4")
+        # fire5
+        net = self._fire(net, 32, 128, "fire5")
+        # fire6
+        net = self._fire(net, 48, 192, "fire6")
+        # fire7
+        net = self._fire(net, 48, 192, "fire7")
+        # fire8
+        net = self._fire(net, 64, 256, "fire8")
+        # maxpool8
+        net = tf.layers.max_pooling2d(net, [3, 3], strides=[2, 2],
+                                      name="maxpool8")
+        # fire9
+        net = self._fire(net, 64, 256, "fire9")
+        # conv10
+        net = tf.layers.conv2d(net, 1000, [1, 1], strides=[1, 1],
+                               padding="SAME", activation=tf.nn.relu,
+                               name="conv10")
+        # avgpool10
+        net = tf.layers.average_pooling2d(net, [13, 13], strides=[1, 1],
+                                          name="avgpool10")
+        # squeeze the axis
+        net = tf.squeeze(net, axis=[1, 2])
+
+        self.logits = net
+        self.prediction = tf.nn.softmax(net)
+
+
+    def _fire(self, inputs, squeeze_depth, expand_depth, scope):
+        with tf.variable_scope(scope):
+            squeeze = tf.layers.conv2d(inputs, squeeze_depth, [1, 1],
+                                       strides=[1, 1], padding="SAME",
+                                       activation=tf.nn.relu, name="squeeze")
+            # squeeze
+            expand_1x1 = tf.layers.conv2d(squeeze, expand_depth, [1, 1],
+                                          strides=[1, 1], padding="SAME",
+                                          activation=tf.nn.relu, name="expand_1x1")
+            expand_3x3 = tf.layers.conv2d(squeeze, expand_depth, [3, 3],
+                                          strides=[1, 1], padding="SAME",
+                                          activation=tf.nn.relu, name="expand_3x3")
+            return tf.concat([expand_1x1, expand_3x3], axis=3)
+
+
+if __name__ == "__main__":
+    inputs = tf.random_normal([32, 224, 224, 3])
+    net = SqueezeNet(inputs)
+    print(net.prediction)