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Fix TensorFlow API usage to be compatible for v0.x and v1.x#36

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19 changes: 13 additions & 6 deletionsSeason1/10-11/dp.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -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

Expand DownExpand Up@@ -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
Expand All@@ -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.
Expand All@@ -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):
'''
Expand Down
23 changes: 15 additions & 8 deletionsSeason1/12-15/dp.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -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

Expand DownExpand Up@@ -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):
Expand All@@ -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'):
Expand DownExpand Up@@ -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'):
Expand All@@ -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):
'''
Expand Down
18 changes: 12 additions & 6 deletionsSeason1/12-15/dp_refined_api.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -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):
Expand DownExpand Up@@ -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):
Expand DownExpand Up@@ -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'):
Expand All@@ -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):
'''
Expand DownExpand Up@@ -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))
18 changes: 12 additions & 6 deletionsSeason1/16-19/dp.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
Expand DownExpand Up@@ -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
Expand DownExpand Up@@ -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)
Expand DownExpand Up@@ -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):
'''
Expand DownExpand Up@@ -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))
18 changes: 12 additions & 6 deletionsSeason1/20/dp.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
Expand DownExpand Up@@ -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
Expand DownExpand Up@@ -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)
Expand DownExpand Up@@ -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())
Expand DownExpand Up@@ -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:')
Expand Down
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