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model.py

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+import tensorflow.compat.v1 as tf
+import utils
+tf.compat.v1.disable_eager_execution()
+
+def get_model(is_train=False, keep_prob=0.8, alpha=0.8):
+    with tf.variable_scope("model", reuse=not is_train):
+        tf_x = tf.placeholder(tf.float32, shape=[None, utils.IMAGE_HEIGHT, utils.IMAGE_WIDTH, utils.IMAGE_CHANNEL])
+        tf_y = tf.placeholder(tf.int32, shape=[None])
+        tf_y_one_hot = tf.one_hot(tf_y, utils.IMAGE_CLASSIFY)
+
+        # conv 1
+        filter_1 = tf.Variable(tf.truncated_normal(shape=[5, 5, 3, 32], mean=utils.mu, stddev=utils.sigma))
+        bias_1 = tf.Variable(tf.constant(0.1, shape=[32]))
+        conv_1 = tf.nn.conv2d(tf_x, filter=filter_1, strides=[1, 2, 2, 1], padding='SAME') + bias_1
+        leaky_relu_1 = tf.nn.leaky_relu(conv_1, alpha=alpha)
+
+        # conv 2
+        filter_2 = tf.Variable(tf.truncated_normal(shape=[3, 3, 32, 48], mean=utils.mu, stddev=utils.sigma))
+        bias_2 = tf.Variable(tf.constant(0.1, shape=[48]))
+        conv_2 = tf.nn.conv2d(leaky_relu_1, filter=filter_2, strides=[1, 2, 2, 1], padding='SAME') + bias_2
+        leaky_relu_2 = tf.nn.leaky_relu(conv_2, alpha=alpha)
+
+        # conv 3
+        filter_3 = tf.Variable(tf.truncated_normal(shape=[3, 3, 48, 64], mean=utils.mu, stddev=utils.sigma))
+        bias_3 = tf.Variable(tf.constant(0.1, shape=[64]))
+        conv_3 = tf.nn.conv2d(leaky_relu_2, filter=filter_3, strides=[1, 2, 2, 1], padding='SAME') + bias_3
+        leaky_relu_3 = tf.nn.leaky_relu(conv_3, alpha=alpha)
+
+        dropout = tf.nn.dropout(leaky_relu_3, keep_prob=keep_prob)
+
+        # flatten，Flatten层用来将输入“压平”，即把多维的输入一维化，常用在从卷积层到全连接层的过渡
+        shape = dropout.get_shape().as_list()
+        flatten_size = shape[1] * shape[2] * shape[3]
+        flatten = tf.reshape(dropout, [-1, flatten_size])
+
+        # fc 1
+        filter_4 = tf.Variable(tf.truncated_normal(shape=[flatten.get_shape().as_list()[1], 100],
+                                                   mean=utils.mu, stddev=utils.sigma))
+        bias_4 = tf.Variable(tf.constant(0.1, shape=[100]))
+        fc_1 = tf.matmul(flatten, filter_4) + bias_4
+        leaky_relu_4 = tf.nn.leaky_relu(fc_1, alpha=alpha)
+
+        # fc 2
+        filter_5 = tf.Variable(tf.truncated_normal(shape=[100, 50], mean=utils.mu, stddev=utils.sigma))
+        bias_5 = tf.Variable(tf.constant(0.1, shape=[50]))
+        fc_2 = tf.matmul(leaky_relu_4, filter_5) + bias_5
+        leaky_relu_5 = tf.nn.leaky_relu(fc_2, alpha=alpha)
+
+        # fc 3
+        filter_6 = tf.Variable(tf.truncated_normal(shape=[50, 10], mean=utils.mu, stddev=utils.sigma))
+        bias_6 = tf.Variable(tf.constant(0.1, shape=[10]))
+        fc_3 = tf.matmul(leaky_relu_5, filter_6) + bias_6
+        leaky_relu_6 = tf.nn.leaky_relu(fc_3, alpha=alpha)
+
+        # result
+        filter_7 = tf.Variable(tf.truncated_normal(shape=[10, utils.IMAGE_CLASSIFY],
+                                                   mean=utils.mu, stddev=utils.sigma))
+        bias_7 = tf.Variable(tf.constant(0.1, shape=[utils.IMAGE_CLASSIFY]))
+        result = tf.matmul(leaky_relu_6, filter_7) + bias_7
+
+        last_layer = result if is_train else tf.nn.softmax(result)
+        return tf_x, tf_y, tf_y_one_hot, last_layer