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models/cnn_net.py

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+
+'''
+
+
+
+conv_1 = slim.conv2d(images, 64, [3, 3], 1, padding='SAME', scope='conv1')
+# (inputs,num_outputs,[卷积核个数] kernel_size,[卷积核的高度，卷积核的宽]stride=1,padding='SAME',)
+max_pool_1 = slim.max_pool2d(conv_1, [2, 2], [2, 2], padding='SAME')
+conv_2 = slim.conv2d(max_pool_1, 128, [3, 3], padding='SAME', scope='conv2')
+max_pool_2 = slim.max_pool2d(conv_2, [2, 2], [2, 2], padding='SAME')
+conv_3 = slim.conv2d(max_pool_2, 256, [3, 3], padding='SAME', scope='conv3')
+max_pool_3 = slim.max_pool2d(conv_3, [2, 2], [2, 2], padding='SAME')
+
+flatten = slim.flatten(max_pool_3)
+fc1 = slim.fully_connected(tf.nn.dropout(flatten, keep_prob), 1024, activation_fn=tf.nn.tanh, scope='fc1')
+logits = slim.fully_connected(tf.nn.dropout(fc1, keep_prob), FLAGS.charset_size, activation_fn=None, scope='fc2')
+# logits = slim.fully_connected(flatten, FLAGS.charset_size, activation_fn=None, reuse=reuse, scope='fc')
+loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))
+# y表示的是实际类别，y_表示预测结果，这实际上面是把原来的神经网络输出层的softmax和cross_entrop何在一起计算，为了追求速度
+accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), labels), tf.float32))
+'''
+
+import tensorflow as tf
+
+
+class CNNNet(tf.keras.Model):
+
+    def __init__(self.):
+        pass