Add exam.6

exam/step6/VGGNetCompleted.py

+import tensorflow as tf
+
+# def generateModel():
+    #----以下是答案部分 begin----#
+
+# 定义placeholder 开始
+Input = tf.placeholder(shape=(None, 224, 224, 3), dtype=tf.float32)
+keep_prob = tf.placeholder(tf.float32, shape=())
+Labels = tf.placeholder(shape=(None, 4), dtype=tf.float32)
+# 定义placeholder 结束
+
+# 第一部分
+Part1_Conv1 = tf.layers.conv2d(inputs=Input,filters=64, kernel_size=3,strides=1,padding='same',activation=tf.nn.relu)
+Part1_Conv2 = tf.layers.conv2d(inputs=Part1_Conv1, filters=64, kernel_size=3 ,strides=1,padding='same',activation=tf.nn.relu)
+Part1_pool1 = tf.layers.max_pooling2d(Part1_Conv2, pool_size=2, strides=2,padding='same')
+
+# 第二部分
+Part2_Conv1 = tf.layers.conv2d(inputs=Part1_pool1, filters=128, kernel_size=3,strides=1,padding='same',activation=tf.nn.relu)
+Part2_Conv2 = tf.layers.conv2d(inputs=Part2_Conv1, filters=128, kernel_size=3,strides=1,padding='same',activation=tf.nn.relu)
+Part2_pool1 = tf.layers.max_pooling2d(Part2_Conv2, pool_size=2, strides=2,padding='same')
+#
+# 第三部分
+Part3_Conv1 = tf.layers.conv2d(inputs=Part2_pool1, filters=256, kernel_size=3,strides=1,padding='same',activation=tf.nn.relu)
+Part3_Conv2 = tf.layers.conv2d(inputs=Part3_Conv1, filters=256, kernel_size=3,strides=1,padding='same',activation=tf.nn.relu)
+Part3_Conv3 = tf.layers.conv2d(inputs=Part3_Conv2, filters=256, kernel_size=1,strides=1,padding='same',activation=tf.nn.relu)
+Part3_pool1 = tf.layers.max_pooling2d(Part3_Conv3, pool_size=2, strides=2,padding='same')
+
+# 第四部分
+Part4_Conv1 = tf.layers.conv2d(inputs=Part3_pool1, filters=512, kernel_size=3,strides=1,padding='same',activation=tf.nn.relu)
+Part4_Conv2 = tf.layers.conv2d(inputs=Part4_Conv1, filters=512, kernel_size=3,strides=1,padding='same',activation=tf.nn.relu)
+Part4_Conv3 = tf.layers.conv2d(inputs=Part4_Conv2, filters=512, kernel_size=1,strides=1,padding='same',activation=tf.nn.relu)
+Part4_pool1 = tf.layers.max_pooling2d(Part4_Conv3, pool_size=2, strides=2,padding='same')
+
+Part5_Conv1 = tf.layers.conv2d(inputs=Part4_pool1, filters=512, kernel_size=3,strides=1,padding='same',activation=tf.nn.relu)
+Part5_Conv2 = tf.layers.conv2d(inputs=Part5_Conv1, filters=512, kernel_size=3,strides=1,padding='same',activation=tf.nn.relu)
+Part5_Conv3 = tf.layers.conv2d(inputs=Part5_Conv2, filters=512, kernel_size=1,strides=1,padding='same',activation=tf.nn.relu)
+Part5_pool1 = tf.layers.max_pooling2d(Part5_Conv3, pool_size=2, strides=2,padding='same')
+
+# 全连接部分
+# flatten扁平化
+flattened = tf.layers.flatten(Part5_pool1)
+# 同样本任务不宜太宽的网络，该层要求使用512个神经元
+dense1 = tf.layers.dense(flattened, units=512,activation=tf.nn.relu)
+dropout1 = tf.nn.dropout(dense1, keep_prob)
+# 该层要求使用256个神经元
+dense2 = tf.layers.dense(dropout1, units=256,activation=tf.nn.relu)
+dropout2 = tf.nn.dropout(dense2, keep_prob)
+outputs = tf.layers.dense(dropout2, units=4)
+
+loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=outputs, labels=Labels))
+train = tf.train.AdamOptimizer().minimize(loss)
+
+#----以上是答案部分 end----#
+
+with tf.Session() as sess:
+    sess.run(tf.global_variables_initializer())
+    # saver.save(sess, \"modelInfo/VGGNet\")
+    tf.train.export_meta_graph(filename=\"step6/modelInfo/VGGNet\",
+                               graph=tf.get_default_graph())
+tf.reset_default_graph()
+# if __name__ == '__main__':
+#     generateModel()
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exam/step6/VGGNetForUsers.py

+import tensorflow as tf
+# 所有的卷积层：卷积核大小为3,步长为1,需要padding,激活函数是relu
+# 所有的池化层：为最大池化,池化范围是2,步长也为2,需要padding
+# 中间全连接层：激活函数是relu
+# 两个隐藏全连接层神经元数分别是512,256
+# 全连接层之间需要dropout
+# dropout使用tf.nn,其它层全部使用tf.layers
+
+# 损失: tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2())
+# 优化器: tf.train.AdamOptimizer()
+
+#********** Begin **********#
+
+
+#********** End **********#
+
+#---以下代码不要改动---#
+#---否则影响测评---#
+with tf.Session() as sess:
+    sess.run(tf.global_variables_initializer())
+    tf.train.export_meta_graph(filename='step6/userModelInfo/VGGNet',
+                               graph=tf.get_default_graph())

exam/step6/VGGNetTest.py

+import os
+os.environ[\"TF_CPP_MIN_LOG_LEVEL\"]='3'
+import warnings
+warnings.filterwarnings('ignore')
+
+import VGGNetCompleted
+
+import VGGNetForUsers
+
+
+rightModelPath = 'step6/modelInfo/VGGNet'
+userModelPath = 'step6/userModelInfo/VGGNet'
+
+# print(os.path.exists(rightModelPath))
+# print(os.path.exists(userModelPath))
+# print(os.path.getsize(rightModelPath))
+# print(os.path.getsize(userModelPath))
+
+
+isRight = os.path.getsize(rightModelPath)==os.path.getsize(userModelPath)
+# print(isRight)
+if isRight:
+    print('恭喜你通过本关测试!VGGNet的结构你已经掌握!',end='')
+else:
+    print('未能通过本关测试,模型结构有误!')
+"
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