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

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

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a419609
API Update to adapt to tf v1.x
TITAN-PyCompat Apr 17, 2019
3317bea
API Update to adapt to tf v1.x
TITAN-PyCompat Apr 17, 2019
cbc2195
API Update to adapt to tf v1.x
TITAN-PyCompat Apr 17, 2019
57f6dc4
API Update to adapt to tf v1.x
TITAN-PyCompat Apr 17, 2019
98bc4c0
API Update to adapt to tf v1.x
TITAN-PyCompat Apr 17, 2019
48717cd
API Update to adapt to tf v1.x
TITAN-PyCompat Apr 17, 2019
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19 changes: 13 additions & 6 deletions Season1/10-11/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 deletions Season1/12-15/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 deletions Season1/12-15/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 deletions Season1/16-19/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 deletions Season1/20/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
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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

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TITAN-PyCompat
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Fix TensorFlow API usage to be compatible for v0.x and v1.x
#36
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6 commits


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a419609

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
3317bea

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
cbc2195

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
57f6dc4

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
98bc4c0

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
48717cd

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019

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19 changes: 13 additions & 6 deletions
Season1/10-11/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 deletions
Season1/12-15/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 deletions
Season1/12-15/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 deletions
Season1/16-19/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 deletions
Season1/20/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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if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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Fix TensorFlow API usage to be compatible for v0.x and v1.x#36

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Fix TensorFlow API usage to be compatible for v0.x and v1.x
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a419609

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
3317bea

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
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API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
57f6dc4

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
98bc4c0

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
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API Update to adapt to tf v1.x


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19 changes: 13 additions & 6 deletions
Season1/10-11/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 deletions
Season1/12-15/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 deletions
Season1/12-15/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 deletions
Season1/16-19/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 deletions
Season1/20/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

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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

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Fix TensorFlow API usage to be compatible for v0.x and v1.x
#36
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a419609

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
3317bea

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
cbc2195

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
57f6dc4

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
98bc4c0

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
48717cd

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019

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19 changes: 13 additions & 6 deletions
Season1/10-11/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 deletions
Season1/12-15/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 deletions
Season1/12-15/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 deletions
Season1/16-19/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 deletions
Season1/20/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

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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

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 Open

TITAN-PyCompat
wants to merge
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commits into

base:master
Choose a base branch

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Fix TensorFlow API usage to be compatible for v0.x and v1.x
#36
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a419609

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
3317bea

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
cbc2195

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
57f6dc4

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
98bc4c0

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
48717cd

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019

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19 changes: 13 additions & 6 deletions
Season1/10-11/dp.py

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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 deletions
Season1/12-15/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 deletions
Season1/12-15/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 deletions
Season1/16-19/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 deletions
Season1/20/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

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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

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Fix TensorFlow API usage to be compatible for v0.x and v1.x
#36
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a419609

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
3317bea

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
cbc2195

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
57f6dc4

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
98bc4c0

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
48717cd

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019

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19 changes: 13 additions & 6 deletions
Season1/10-11/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 deletions
Season1/12-15/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 deletions
Season1/12-15/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 deletions
Season1/16-19/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 deletions
Season1/20/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

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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

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Fix TensorFlow API usage to be compatible for v0.x and v1.x
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a419609

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
3317bea

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
cbc2195

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
57f6dc4

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
98bc4c0

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
48717cd

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019

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19 changes: 13 additions & 6 deletions
Season1/10-11/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 deletions
Season1/12-15/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 deletions
Season1/12-15/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 deletions
Season1/16-19/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 deletions
Season1/20/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

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Fix TensorFlow API usage to be compatible for v0.x and v1.x
#36
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a419609

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
3317bea

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
cbc2195

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
57f6dc4

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
98bc4c0

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019
48717cd

API Update to adapt to tf v1.x


TITAN-PyCompat Apr 17, 2019

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19 changes: 13 additions & 6 deletions
Season1/10-11/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 deletions
Season1/12-15/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 deletions
Season1/12-15/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 deletions
Season1/16-19/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 deletions
Season1/20/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