fixes

deep_prior.py

@@ -0,0 +1,67 @@
+import os
+import scipy.misc
+import numpy as np
+import tensorflow as tf
+from tensorflow.contrib.layers import conv2d, conv2d_transpose
+
+
+def pool(X):
+	return tf.nn.max_pool(X, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+
+
+def uppool(X):
+	height, width = X.get_shape().as_list()[1:3]
+	return tf.image.resize_images(X, (height * 2, width * 2))
+
+
+# download from https://i.imgur.com/ytjR2QF.png
+image = scipy.misc.imread("snail256.png").astype(np.float32) / 255.0
+
+
+def make_unet(X):
+	depths = [16, 32, 64, 128, 256, 512]
+	# TODO figure out how to make batchnorm work
+
+	activation_fn = tf.nn.tanh
+	# convolve and half image size a few times
+	for depth in depths:
+		# 		X = convolution(X, kernel_size=depth, stride=3, activation_fn=activation_fn)
+		X = conv2d(X, depth, 3, activation_fn=activation_fn)
+		X = pool(X)
+
+	X = conv2d(X, depth, 3, activation_fn=activation_fn)
+
+	# upconcolve and double image size a few times
+	for depth in reversed(depths):
+		X = uppool(X)
+		X = conv2d_transpose(X, depth, 3, activation_fn=activation_fn)
+
+	X = conv2d(X, 3, 3, activation_fn=None)
+
+	return X
+
+
+input = tf.constant(image.reshape((1, 256, 256, 3)))
+
+output = make_unet(input)
+
+loss = tf.reduce_mean(tf.square(input - output))
+
+# TODO L-BFGS-B should be faster here, but could not get it to work
+optimizer = tf.train.AdamOptimizer()
+train_op = optimizer.minimize(loss)
+
+sess = tf.InteractiveSession()
+sess.run(tf.global_variables_initializer())
+
+if not os.path.exists("frames"):
+	os.mkdir("frames")
+
+def save(frame):
+		scipy.misc.imsave("frames/%d.png" % frame, sess.run(output).reshape((256, 256, 3)).clip(0, 1))
+
+for i in range(10000):
+	print("\r#" + str(i), end='', flush=True)
+	sess.run(train_op)
+	if not i % 100:
+		save(i/100)

extensions.py

@@ -537,7 +537,7 @@ def wrap(self):
 		return str(self)  # TODO!?
 
 
-# WOW YAY WORKS!!!!!
+# WOW YAY WORKS!!!
 # ONLY VIA EXPLICIT CONSTRUCTOR!
 # NOOOO!! BAAAD! isinstance(my_xlist,list) FALSE !!
 
@@ -728,10 +728,10 @@ def fix_int(self, i):
 	def character(self, nr):
 		return self.item(nr)
 
-	def item(self, nr):  # -1 AppleScript style !!! BUT list[0] !!!
+	def item(self, nr):  # -1 AppleScript style ! BUT list[0] !
 		return self[xlist(self).fix_int(nr)]
 
-	def word(self, nr):  # -1 AppleScript style !!! BUT list[0] !!!):
+	def word(self, nr):  # -1 AppleScript style ! BUT list[0] !):
 		return self[xlist(self).fix_int(nr)]
 
 	def invert(self):  # ! Self modifying !
@@ -920,7 +920,7 @@ def __sub__(self, x):
 	def synsets(self, param):
 		pass
 
-	def is_noun(self):  # expensive!!!):
+	def is_noun(self):  # expensive!):
 		# Sequel::InvalidOperation Invalid argument used for IS operator
 		return self.synsets('noun') or self.gsub(r's$', "").synsets('noun')  # except False
 
@@ -1480,7 +1480,7 @@ def find_class(match=""):  # all
 # def __getattr__(self, attr):
 #     import sys
 #     import math
-#  # ruby method_missing !!!
+#  # ruby method_missing !
 #     import inspect
 #     for name, obj in inspect.getmembers(sys.modules['math']):
 #         if name==attr: return obj

letter.py

@@ -11,7 +11,7 @@
 # overfit = False
 overfit = True
 if overfit:
-	print("using OVERFIT DEBUG DATA!!!")
+	print("using OVERFIT DEBUG DATA!")
 	min_size = 24
 	max_size = 24
 	max_padding = 8

net.py

@@ -442,7 +442,7 @@ def train(self, data=0, steps=-1, dropout=None, display_step=10, test_step=100,
 				acc, summary = session.run([self.accuracy, self.summaries], feed_dict=feed)
 				# self.summary_writer.add_summary(summary, step) # only test summaries for smoother curve
 				print("\rStep {:d} Loss= {:.6f} Accuracy= {:.3f} Time= {:d}s".format(step, loss, acc, seconds), end=' ')
-				if str(loss) == "nan": return print("\nLoss gradiant explosion, exiting!!!")  # restore!
+				if str(loss) == "nan": return print("\nLoss gradiant explosion, exiting!")  # restore!
 			if step % test_step == 0: self.test(step)
 			if step % save_step == 0 and step > 0:
 				print("SAVING snapshot %s" % snapshot)
@@ -479,20 +479,20 @@ def resume(self, session):
 		checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
 		if checkpoint:
 			if self.name and not self.name in checkpoint:
-				print("IGNORING checkpoint of other run : " + checkpoint + " !!!")
+				print("IGNORING checkpoint of other run : " + checkpoint + " !")
 				checkpoint = None
 		else:
 			print("NO checkpoint, nothing to resume")
 		if checkpoint:
-			print("LOADING " + checkpoint + " !!!")
+			print("LOADING " + checkpoint + " !")
 			try:
 				persister = tf.train.Saver(tf.global_variables())
 				persister.restore(session, checkpoint)
 				print("resume checkpoint successful!")
 				return True
 			except Exception as ex:
 				print(ex)
-				print("CANNOT LOAD checkpoint %s !!!" % checkpoint)
+				print("CANNOT LOAD checkpoint %s !" % checkpoint)
 		return False
 
 	def restore(self):  # name

word_recognizer.py

@@ -6,7 +6,7 @@
 # https://github.com/fchollet/keras/pull/4115/files
 
 # AtrousConv1D = AtrousConvolution1D
-# YAY!!!!!!!!!!
+# YAY!!!!
 
 # https://github.com/nrTQgc/deep-anpr/commit/adfbbe7f3deeaa39bdecc36d8398434b76fdf211
 #  fork confusion : ^^  13 days ago BUUUUT: # https://github.com/nrTQgc/deep-anpr  two months ago!?!?!?!