API Update to adapt to tf v1.x

Season1/10-11/dp.py

@@ -9,6 +9,13 @@
 # 我们自己
 import load
 
+if(tf.__version__.startswith("1.")):
+	merge_all_summaries , scalar_summary , histogram_summary= tf.summary.merge_all , tf.summary.scalar , tf.summary.histogram
+
+else:
+	merge_all_summaries , scalar_summary , histogram_summary = tf.merge_all_summaries , tf.scalar_summary , tf.histogram_summary
+
+
 train_samples, train_labels = load._train_samples, load._train_labels
 test_samples, test_labels = load._test_samples,  load._test_labels
 
@@ -88,17 +95,17 @@ def define_graph(self):
 					tf.truncated_normal([image_size * image_size, self.num_hidden], stddev=0.1), name='fc1_weights'
 				)
 				fc1_biases = tf.Variable(tf.constant(0.1, shape=[self.num_hidden]), name='fc1_biases')
-				tf.histogram_summary('fc1_weights', fc1_weights)
-				tf.histogram_summary('fc1_biases', fc1_biases)
+				histogram_summary('fc1_weights', fc1_weights)
+				histogram_summary('fc1_biases', fc1_biases)
 
 			# fully connected layer 2 --> output layer
 			with tf.name_scope('fc2'):
 				fc2_weights = tf.Variable(
 					tf.truncated_normal([self.num_hidden, num_labels], stddev=0.1), name='fc2_weights'
 				)
 				fc2_biases = tf.Variable(tf.constant(0.1, shape=[num_labels]), name='fc2_biases')
-				tf.histogram_summary('fc2_weights', fc2_weights)
-				tf.histogram_summary('fc2_biases', fc2_biases)
+				histogram_summary('fc2_weights', fc2_weights)
+				histogram_summary('fc2_biases', fc2_biases)
 
 
 			# 想在来定义图谱的运算
@@ -121,7 +128,7 @@ def model(data):
 				self.loss = tf.reduce_mean(
 					tf.nn.softmax_cross_entropy_with_logits(logits, self.tf_train_labels)
 				)
-				tf.scalar_summary('Loss', self.loss)
+				scalar_summary('Loss', self.loss)
 
 
 			# Optimizer.
@@ -133,7 +140,7 @@ def model(data):
 				self.train_prediction = tf.nn.softmax(logits, name='train_prediction')
 				self.test_prediction = tf.nn.softmax(model(self.tf_test_samples), name='test_prediction')
 
-			self.merged = tf.merge_all_summaries()
+			self.merged = merge_all_summaries()
 
 	def run(self):
 		'''

Season1/12-15/dp.py

@@ -9,6 +9,13 @@
 # 我们自己
 import load
 
+if(tf.__version__.startswith("1.")):
+	image_summary , scalar_summary= tf.summary.image , tf.summary.scalar
+	merge_summary , histogram_summary = tf.summary.merge , tf.summary.histogram
+else:
+	image_summary , scalar_summary = tf.image_summary , tf.scalar_summary
+	merge_summary , histogram_summary =	tf.merge_summary , tf.histogram_summary
+
 train_samples, train_labels = load._train_samples, load._train_labels
 test_samples, test_labels = load._test_samples,  load._test_labels
 
@@ -121,15 +128,15 @@ def define_graph(self):
 						[(image_size // down_scale) * (image_size // down_scale) * self.last_conv_depth, self.num_hidden], stddev=0.1))
 				fc1_biases = tf.Variable(tf.constant(0.1, shape=[self.num_hidden]))
 
-				self.train_summaries.append(tf.histogram_summary('fc1_weights', fc1_weights))
-				self.train_summaries.append(tf.histogram_summary('fc1_biases', fc1_biases))
+				self.train_summaries.append(histogram_summary('fc1_weights', fc1_weights))
+				self.train_summaries.append(histogram_summary('fc1_biases', fc1_biases))
 
 			# fully connected layer 2 --> output layer
 			with tf.name_scope('fc2'):
 				fc2_weights = tf.Variable(tf.truncated_normal([self.num_hidden, num_labels], stddev=0.1), name='fc2_weights')
 				fc2_biases = tf.Variable(tf.constant(0.1, shape=[num_labels]), name='fc2_biases')
-				self.train_summaries.append(tf.histogram_summary('fc2_weights', fc2_weights))
-				self.train_summaries.append(tf.histogram_summary('fc2_biases', fc2_biases))
+				self.train_summaries.append(histogram_summary('fc2_weights', fc2_weights))
+				self.train_summaries.append(histogram_summary('fc2_biases', fc2_biases))
 
 			# 想在来定义图谱的运算
 			def model(data, train=True):
@@ -154,7 +161,7 @@ def model(data, train=True):
 						filter_map = hidden[-1]
 						filter_map = tf.transpose(filter_map, perm=[2, 0, 1])
 						filter_map = tf.reshape(filter_map, (self.conv1_depth, 32, 32, 1))
-						self.test_summaries.append(tf.image_summary('conv1_relu', tensor=filter_map, max_images=self.conv1_depth))
+						self.test_summaries.append(image_summary('conv1_relu', tensor=filter_map, max_images=self.conv1_depth))
 
 				with tf.name_scope('conv2_model'):
 					with tf.name_scope('convolution'):
@@ -206,7 +213,7 @@ def model(data, train=True):
 			logits = model(self.tf_train_samples)
 			with tf.name_scope('loss'):
 				self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, self.tf_train_labels))
-				self.train_summaries.append(tf.scalar_summary('Loss', self.loss))
+				self.train_summaries.append(scalar_summary('Loss', self.loss))
 
 			# Optimizer.
 			with tf.name_scope('optimizer'):
@@ -218,8 +225,8 @@ def model(data, train=True):
 			with tf.name_scope('test'):
 				self.test_prediction = tf.nn.softmax(model(self.tf_test_samples, train=False), name='test_prediction')
 
-			self.merged_train_summary = tf.merge_summary(self.train_summaries)
-			self.merged_test_summary = tf.merge_summary(self.test_summaries)
+			self.merged_train_summary = merge_summary(self.train_summaries)
+			self.merged_test_summary = merge_summary(self.test_summaries)
 
 	def run(self):
 		'''

Season1/12-15/dp_refined_api.py

@@ -3,6 +3,12 @@
 from sklearn.metrics import confusion_matrix
 import numpy as np
 
+if(tf.__version__.startswith("1.")):
+	image_summary , scalar_summary= tf.summary.image , tf.summary.scalar
+	merge_summary , histogram_summary = tf.summary.merge , tf.summary.histogram
+else:
+	image_summary , scalar_summary = tf.image_summary , tf.scalar_summary
+	merge_summary , histogram_summary =	tf.merge_summary , tf.histogram_summary
 
 class Network():
 	def __init__(self, train_batch_size, test_batch_size, pooling_scale):
@@ -68,8 +74,8 @@ def add_fc(self, *, in_num_nodes, out_num_nodes, activation='relu', name):
 			biases = tf.Variable(tf.constant(0.1, shape=[out_num_nodes]))
 			self.fc_weights.append(weights)
 			self.fc_biases.append(biases)
-			self.train_summaries.append(tf.histogram_summary(str(len(self.fc_weights))+'_weights', weights))
-			self.train_summaries.append(tf.histogram_summary(str(len(self.fc_biases))+'_biases', biases))
+			self.train_summaries.append(histogram_summary(str(len(self.fc_weights))+'_weights', weights))
+			self.train_summaries.append(histogram_summary(str(len(self.fc_biases))+'_biases', biases))
 
 	# should make the definition as an exposed API, instead of implemented in the function
 	def define_inputs(self, *, train_samples_shape, train_labels_shape, test_samples_shape):
@@ -131,7 +137,7 @@ def model(data_flow, train=True):
 		logits = model(self.tf_train_samples)
 		with tf.name_scope('loss'):
 			self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, self.tf_train_labels))
-			self.train_summaries.append(tf.scalar_summary('Loss', self.loss))
+			self.train_summaries.append(scalar_summary('Loss', self.loss))
 
 		# Optimizer.
 		with tf.name_scope('optimizer'):
@@ -143,8 +149,8 @@ def model(data_flow, train=True):
 		with tf.name_scope('test'):
 			self.test_prediction = tf.nn.softmax(model(self.tf_test_samples, train=False), name='test_prediction')
 
-		self.merged_train_summary = tf.merge_summary(self.train_summaries)
-		self.merged_test_summary = tf.merge_summary(self.test_summaries)
+		self.merged_train_summary = merge_summary(self.train_summaries)
+		self.merged_test_summary = merge_summary(self.test_summaries)
 
 	def run(self, data_iterator, train_samples, train_labels, test_samples, test_labels):
 		'''
@@ -223,4 +229,4 @@ def visualize_filter_map(self, tensor, *, how_many, display_size, name):
 		print(filter_map.get_shape())
 		filter_map = tf.reshape(filter_map, (how_many, display_size, display_size, 1))
 		print(how_many)
-		self.test_summaries.append(tf.image_summary(name, tensor=filter_map, max_images=how_many))
+		self.test_summaries.append(image_summary(name, tensor=filter_map, max_images=how_many))

Season1/16-19/dp.py

@@ -3,6 +3,12 @@
 from sklearn.metrics import confusion_matrix
 import numpy as np
 
+if(tf.__version__.startswith("1.")):
+	image_summary , scalar_summary= tf.summary.image , tf.summary.scalar
+	merge_summary , histogram_summary = tf.summary.merge , tf.summary.histogram
+else:
+	image_summary , scalar_summary = tf.image_summary , tf.scalar_summary
+	merge_summary , histogram_summary =	tf.merge_summary , tf.histogram_summary
 
 class Network():
 	def __init__(self, train_batch_size, test_batch_size, pooling_scale,
@@ -71,8 +77,8 @@ def add_fc(self, *, in_num_nodes, out_num_nodes, activation='relu', name):
 			biases = tf.Variable(tf.constant(0.1, shape=[out_num_nodes]))
 			self.fc_weights.append(weights)
 			self.fc_biases.append(biases)
-			self.train_summaries.append(tf.histogram_summary(str(len(self.fc_weights))+'_weights', weights))
-			self.train_summaries.append(tf.histogram_summary(str(len(self.fc_biases))+'_biases', biases))
+			self.train_summaries.append(histogram_summary(str(len(self.fc_weights))+'_weights', weights))
+			self.train_summaries.append(histogram_summary(str(len(self.fc_biases))+'_biases', biases))
 
 	def apply_regularization(self, _lambda):
 		# L2 regularization for the fully connected parameters
@@ -149,7 +155,7 @@ def model(data_flow, train=True):
 		with tf.name_scope('loss'):
 			self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, self.tf_train_labels))
 			self.loss += self.apply_regularization(_lambda=5e-4)
-			self.train_summaries.append(tf.scalar_summary('Loss', self.loss))
+			self.train_summaries.append(scalar_summary('Loss', self.loss))
 
 		# learning rate decay
 		global_step = tf.Variable(0)
@@ -184,8 +190,8 @@ def model(data_flow, train=True):
 		with tf.name_scope('test'):
 			self.test_prediction = tf.nn.softmax(model(self.tf_test_samples, train=False), name='test_prediction')
 
-		self.merged_train_summary = tf.merge_summary(self.train_summaries)
-		self.merged_test_summary = tf.merge_summary(self.test_summaries)
+		self.merged_train_summary = merge_summary(self.train_summaries)
+		self.merged_test_summary = merge_summary(self.test_summaries)
 
 	def run(self, data_iterator, train_samples, train_labels, test_samples, test_labels):
 		'''
@@ -262,4 +268,4 @@ def visualize_filter_map(self, tensor, *, how_many, display_size, name):
 		print(filter_map.get_shape())
 		filter_map = tf.reshape(filter_map, (how_many, display_size, display_size, 1))
 		print(how_many)
-		self.test_summaries.append(tf.image_summary(name, tensor=filter_map, max_images=how_many))
+		self.test_summaries.append(image_summary(name, tensor=filter_map, max_images=how_many))

Season1/20/dp.py

@@ -3,6 +3,12 @@
 from sklearn.metrics import confusion_matrix
 import numpy as np
 
+if(tf.__version__.startswith("1.")):
+	image_summary , scalar_summary= tf.summary.image , tf.summary.scalar
+	merge_summary , histogram_summary = tf.summary.merge , tf.summary.histogram
+else:
+	image_summary , scalar_summary = tf.image_summary , tf.scalar_summary
+	merge_summary , histogram_summary =	tf.merge_summary , tf.histogram_summary
 
 class Network():
 	def __init__(self, train_batch_size, test_batch_size, pooling_scale,
@@ -80,8 +86,8 @@ def add_fc(self, *, in_num_nodes, out_num_nodes, activation='relu', name):
 			biases = tf.Variable(tf.constant(0.1, shape=[out_num_nodes]))
 			self.fc_weights.append(weights)
 			self.fc_biases.append(biases)
-			self.train_summaries.append(tf.histogram_summary(str(len(self.fc_weights))+'_weights', weights))
-			self.train_summaries.append(tf.histogram_summary(str(len(self.fc_biases))+'_biases', biases))
+			self.train_summaries.append(histogram_summary(str(len(self.fc_weights))+'_weights', weights))
+			self.train_summaries.append(histogram_summary(str(len(self.fc_biases))+'_biases', biases))
 
 	def apply_regularization(self, _lambda):
 		# L2 regularization for the fully connected parameters
@@ -158,7 +164,7 @@ def model(data_flow, train=True):
 		with tf.name_scope('loss'):
 			self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, self.tf_train_labels))
 			self.loss += self.apply_regularization(_lambda=5e-4)
-			self.train_summaries.append(tf.scalar_summary('Loss', self.loss))
+			self.train_summaries.append(scalar_summary('Loss', self.loss))
 
 		# learning rate decay
 		global_step = tf.Variable(0)
@@ -198,8 +204,8 @@ def model(data_flow, train=True):
 			self.single_prediction = tf.nn.softmax(model(single_input, train=False), name='single_prediction')
 			tf.add_to_collection("prediction", self.single_prediction)
 
-		self.merged_train_summary = tf.merge_summary(self.train_summaries)
-		self.merged_test_summary = tf.merge_summary(self.test_summaries)
+		self.merged_train_summary = merge_summary(self.train_summaries)
+		self.merged_test_summary = merge_summary(self.test_summaries)
 
 		# 放在定义Graph之后，保存这张计算图
 		self.saver = tf.train.Saver(tf.all_variables())
@@ -327,7 +333,7 @@ def visualize_filter_map(self, tensor, *, how_many, display_size, name):
 		#print(filter_map.get_shape())
 		filter_map = tf.reshape(filter_map, (how_many, display_size, display_size, 1))
 		#print(how_many)
-		self.test_summaries.append(tf.image_summary(name, tensor=filter_map, max_images=how_many))
+		self.test_summaries.append(image_summary(name, tensor=filter_map, max_images=how_many))
 
 	def print_confusion_matrix(self, confusionMatrix):
 		print('Confusion    Matrix:')