update to 7.0.1

examples/charcnn_text_classifier.py

@@ -1,36 +1,18 @@
 
 import tensorflow as tf
-<<<<<<< HEAD
 import tensorgraph as tg
 from tensorgraph.layers import Reshape, Embedding, Conv2D, RELU, Linear, Flatten, ReduceSum, Softmax
 from nltk.tokenize import RegexpTokenizer
 from nlpbox import CharNumberEncoder, CatNumberEncoder
 from tensorgraph.utils import valid, split_df, make_one_hot
 from tensorgraph.cost import entropy, accuracy
-=======
-import tensorgraphx as tg
-from tensorgraphx.layers import Reshape, Embedding, Conv2D, RELU, Linear, Flatten, ReduceSum, Softmax
-from nltk.tokenize import RegexpTokenizer
-from nlpbox import CharNumberEncoder, CatNumberEncoder
-from tensorgraphx.utils import valid, split_df, make_one_hot
-from tensorgraphx.cost import entropy, accuracy
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
 import pandas
 import numpy as np
 
 # character CNN
 def model(word_len, sent_len, nclass):
     unicode_size = 1000
     ch_embed_dim = 20
-
-<<<<<<< HEAD
-=======
-    h, w = valid(ch_embed_dim, word_len, stride=(1,1), kernel_size=(ch_embed_dim,5))
-    h, w = valid(h, w, stride=(1,1), kernel_size=(1,5))
-    h, w = valid(h, w, stride=(1,2), kernel_size=(1,5))
-    conv_out_dim = int(h * w * 60)
-
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
     X_ph = tf.placeholder('int32', [None, sent_len, word_len])
     input_sn = tg.StartNode(input_vars=[X_ph])
     charcnn_hn = tg.HiddenNode(prev=[input_sn],
@@ -39,7 +21,6 @@ def model(word_len, sent_len, nclass):
                                               encode_dim=ch_embed_dim,
                                               zero_pad=True),
                                     Reshape(shape=(-1, ch_embed_dim, word_len, 1)),
-<<<<<<< HEAD
                                     Conv2D(num_filters=20, padding='VALID',
                                            kernel_size=(ch_embed_dim,5), stride=(1,1)),
                                     RELU(),
@@ -51,19 +32,6 @@ def model(word_len, sent_len, nclass):
                                     RELU(),
                                     Flatten(),
                                     Linear(nclass),
-=======
-                                    Conv2D(input_channels=1, num_filters=20, padding='VALID',
-                                           kernel_size=(ch_embed_dim,5), stride=(1,1)),
-                                    RELU(),
-                                    Conv2D(input_channels=20, num_filters=40, padding='VALID',
-                                           kernel_size=(1,5), stride=(1,1)),
-                                    RELU(),
-                                    Conv2D(input_channels=40, num_filters=60, padding='VALID',
-                                           kernel_size=(1,5), stride=(1,2)),
-                                    RELU(),
-                                    Flatten(),
-                                    Linear(conv_out_dim, nclass),
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
                                     Reshape((-1, sent_len, nclass)),
                                     ReduceSum(1),
                                     Softmax()
@@ -101,11 +69,7 @@ def tweets(word_len, sent_len, train_valid_ratio=[5,1]):
 
 
 def train():
-<<<<<<< HEAD
     from tensorgraph.trainobject import train as mytrain
-=======
-    from tensorgraphx.trainobject import train as mytrain
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
     with tf.Session() as sess:
         word_len = 20
         sent_len = 50

examples/cifar10_allcnn.py

@@ -7,7 +7,6 @@
 
 from __future__ import division, print_function, absolute_import
 
-<<<<<<< HEAD
 from tensorgraph.layers import Conv2D, RELU, MaxPooling, LRN, Tanh, Dropout, \
                                Softmax, Flatten, Linear, AvgPooling, \
                                Lambda, BatchNormalization, IdentityBlock, \
@@ -17,25 +16,13 @@
 import tensorflow as tf
 from tensorgraph.cost import entropy, accuracy, mse
 from tensorgraph.dataset import Mnist, Cifar10
-=======
-from tensorgraphx.layers import Conv2D, RELU, MaxPooling, LRN, Tanh, Dropout, \
-                               Softmax, Flatten, Linear, AvgPooling, \
-                               Lambda, BatchNormalization, IdentityBlock, \
-                               TransitionLayer, DenseNet
-from tensorgraphx.utils import same, valid, same_nd, valid_nd
-import tensorgraphx as tg
-import tensorflow as tf
-from tensorgraphx.cost import entropy, accuracy, mse
-from tensorgraphx.dataset import Mnist, Cifar10
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
 from tensorflow.python.framework import ops
 import numpy as np
 
 
 def model(nclass, h, w, c):
     with tf.name_scope('Cifar10AllCNN'):
         seq = tg.Sequential()
-<<<<<<< HEAD
         seq.add(Conv2D(num_filters=96, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
         seq.add(RELU())
         seq.add(BatchNormalization())
@@ -73,54 +60,6 @@ def model(nclass, h, w, c):
         seq.add(BatchNormalization())
 
         seq.add(AvgPooling(poolsize=(8, 8), stride=(1,1), padding='VALID'))
-=======
-        seq.add(Conv2D(input_channels=c, num_filters=96, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
-        seq.add(RELU())
-        h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(3,3))
-        seq.add(BatchNormalization(input_shape=[h,w,96]))
-
-        seq.add(Conv2D(input_channels=96, num_filters=96, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
-        seq.add(RELU())
-        h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(3,3))
-        seq.add(Dropout(0.5))
-
-        seq.add(Conv2D(input_channels=96, num_filters=96, kernel_size=(3, 3), stride=(2, 2), padding='SAME'))
-        seq.add(RELU())
-        h, w = same(in_height=h, in_width=w, stride=(2,2), kernel_size=(3,3))
-        seq.add(BatchNormalization(input_shape=[h,w,96]))
-
-        seq.add(Conv2D(input_channels=96, num_filters=192, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
-        seq.add(RELU())
-        h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(3,3))
-        seq.add(Dropout(0.5))
-
-        seq.add(Conv2D(input_channels=192, num_filters=192, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
-        seq.add(RELU())
-        h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(3,3))
-        seq.add(BatchNormalization(input_shape=[h,w,192]))
-
-        seq.add(Conv2D(input_channels=192, num_filters=192, kernel_size=(3, 3), stride=(2, 2), padding='SAME'))
-        seq.add(RELU())
-        h, w = same(in_height=h, in_width=w, stride=(2,2), kernel_size=(3,3))
-        seq.add(Dropout(0.5))
-
-        seq.add(Conv2D(input_channels=192, num_filters=192, kernel_size=(3, 3), stride=(1, 1), padding='SAME'))
-        seq.add(RELU())
-        h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(3,3))
-        seq.add(BatchNormalization(input_shape=[h,w,192]))
-
-        seq.add(Conv2D(input_channels=192, num_filters=192, kernel_size=(1, 1), stride=(1, 1), padding='SAME'))
-        seq.add(RELU())
-        h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(1,1))
-        seq.add(Dropout(0.5))
-
-        seq.add(Conv2D(input_channels=192, num_filters=nclass, kernel_size=(1, 1), stride=(1, 1), padding='SAME'))
-        seq.add(RELU())
-        h, w = same(in_height=h, in_width=w, stride=(1,1), kernel_size=(1,1))
-        seq.add(BatchNormalization(input_shape=[h,w,nclass]))
-
-        seq.add(AvgPooling(poolsize=(h, w), stride=(1,1), padding='VALID'))
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
         seq.add(Flatten())
         seq.add(Softmax())
     return seq
@@ -210,11 +149,7 @@ def train():
 
 
 def train_with_trainobject():
-<<<<<<< HEAD
     from tensorgraph.trainobject import train as mytrain
-=======
-    from tensorgraphx.trainobject import train as mytrain
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
     config = tf.ConfigProto()
     config.gpu_options.allow_growth = True
     with tf.Session(config = config) as sess:
@@ -243,11 +178,7 @@ def train_with_trainobject():
 
 
 def train_with_VGG():
-<<<<<<< HEAD
     from tensorgraph.trainobject import train as mytrain
-=======
-    from tensorgraphx.trainobject import train as mytrain
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
     config = tf.ConfigProto()
     config.gpu_options.allow_growth = True
     with tf.Session(config = config) as sess:
@@ -256,21 +187,12 @@ def train_with_VGG():
         _, nclass = y_train.shape
         print('X max', np.max(X_train))
         print('X min', np.min(X_train))
-<<<<<<< HEAD
         from tensorgraph.layers import VGG19
         seq = tg.Sequential()
         layer = VGG19()
         seq.add(layer)
         seq.add(Flatten())
         seq.add(Linear(nclass))
-=======
-        from tensorgraphx.layers import VGG19
-        seq = tg.Sequential()
-        layer = VGG19(input_channels=c, input_shape=(h,w))
-        seq.add(layer)
-        seq.add(Flatten())
-        seq.add(Linear(512,nclass))
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
         seq.add(Softmax())
         X_ph = tf.placeholder('float32', [None, h, w, c])
         y_ph = tf.placeholder('float32', [None, nclass])
@@ -292,11 +214,7 @@ def train_with_VGG():
 
 
 def train_with_Resnet():
-<<<<<<< HEAD
     from tensorgraph.trainobject import train as mytrain
-=======
-    from tensorgraphx.trainobject import train as mytrain
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
     config = tf.ConfigProto()
     config.gpu_options.allow_growth = True
     with tf.Session(config = config) as sess:
@@ -306,28 +224,12 @@ def train_with_Resnet():
         print('X max', np.max(X_train))
         print('X min', np.min(X_train))
         seq = tg.Sequential()
-<<<<<<< HEAD
         seq.add(IdentityBlock(nlayers=4, filters=[32, 64]))
         seq.add(TransitionLayer(16))
         seq.add(IdentityBlock(nlayers=4, filters=[64, 128]))
         seq.add(TransitionLayer(16))
         seq.add(Flatten())
         seq.add(Linear(nclass))
-=======
-        id1 = IdentityBlock(input_channels=c, input_shape=(h,w), nlayers=4, filters=[32, 64])
-        seq.add(id1)
-        trans1 = TransitionLayer(input_channels=id1.output_channels, input_shape=id1.output_shape)
-        seq.add(trans1)
-
-        id2 = IdentityBlock(input_channels=trans1.output_channels, input_shape=trans1.output_shape,
-                            nlayers=4, filters=[64, 128])
-        seq.add(id2)
-        trans2 = TransitionLayer(input_channels=id2.output_channels, input_shape=id2.output_shape)
-        seq.add(trans2)
-        seq.add(Flatten())
-        ldim = trans2.output_channels * np.prod(trans2.output_shape)
-        seq.add(Linear(ldim,nclass))
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
         seq.add(Softmax())
 
         X_ph = tf.placeholder('float32', [None, h, w, c])
@@ -350,11 +252,7 @@ def train_with_Resnet():
 
 
 def train_with_Densenet():
-<<<<<<< HEAD
     from tensorgraph.trainobject import train as mytrain
-=======
-    from tensorgraphx.trainobject import train as mytrain
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
     config = tf.ConfigProto()
     config.gpu_options.allow_growth = True
     with tf.Session(config = config) as sess:
@@ -364,17 +262,9 @@ def train_with_Densenet():
         print('X max', np.max(X_train))
         print('X min', np.min(X_train))
         seq = tg.Sequential()
-<<<<<<< HEAD
         seq.add(DenseNet(ndense=3, growth_rate=4, nlayer1blk=4))
         seq.add(Flatten())
         seq.add(Linear(nclass))
-=======
-        dense = DenseNet(input_channels=c, input_shape=(h,w), ndense=3, growth_rate=4, nlayer1blk=4)
-        seq.add(dense)
-        seq.add(Flatten())
-        ldim = dense.output_channels
-        seq.add(Linear(ldim,nclass))
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
         seq.add(Softmax())
 
         X_ph = tf.placeholder('float32', [None, h, w, c])

examples/example.py

@@ -1,15 +1,9 @@
 
 import tensorflow as tf
 import numpy as np
-<<<<<<< HEAD
 from tensorgraph import Graph, StartNode, HiddenNode, EndNode
 from tensorgraph.layers import Linear, RELU, Concat, Mean, Sum
 from tensorgraph import ProgressBar, SequentialIterator
-=======
-from tensorgraphx import Graph, StartNode, HiddenNode, EndNode
-from tensorgraphx.layers import Linear, RELU, Concat, Mean, Sum
-from tensorgraphx import ProgressBar, SequentialIterator
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
 
 
 def model():
@@ -25,21 +19,12 @@ def model():
 
     h1 = HiddenNode(prev=[start1, start2],
                     input_merge_mode=Concat(),
-<<<<<<< HEAD
                     layers=[Linear(y2_dim), RELU()])
     h2 = HiddenNode(prev=[start2],
                     layers=[Linear(y2_dim), RELU()])
     h3 = HiddenNode(prev=[h1, h2],
                     input_merge_mode=Sum(),
                     layers=[Linear(y1_dim), RELU()])
-=======
-                    layers=[Linear(y1_dim+y2_dim, y2_dim), RELU()])
-    h2 = HiddenNode(prev=[start2],
-                    layers=[Linear(y2_dim, y2_dim), RELU()])
-    h3 = HiddenNode(prev=[h1, h2],
-                    input_merge_mode=Sum(),
-                    layers=[Linear(y2_dim, y1_dim), RELU()])
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
     e1 = EndNode(prev=[h3])
     e2 = EndNode(prev=[h2])
 
@@ -74,11 +59,7 @@ def train():
                 n_exp += len(y1_batch)
                 pbar.update(n_exp)
             print('end')
-<<<<<<< HEAD
         # saver.save(sess, 'test.tf')
-=======
-        saver.save(sess, 'test.tf')
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
 
 
 if __name__ == '__main__':

examples/hierachical_softmax.py

@@ -1,5 +1,4 @@
 
-<<<<<<< HEAD
 from tensorgraph.node import StartNode, HiddenNode, EndNode
 import tensorflow as tf
 from tensorgraph.layers.linear import Linear
@@ -8,16 +7,7 @@
 from tensorgraph.graph import Graph
 import numpy as np
 from tensorgraph.data_iterator import SequentialIterator
-=======
-from tensorgraphx.node import StartNode, HiddenNode, EndNode
-import tensorflow as tf
-from tensorgraphx.layers.linear import Linear
-from tensorgraphx.layers.activation import RELU, Softmax
-from tensorgraphx.layers.merge import Concat, Mean, Sum
-from tensorgraphx.graph import Graph
-import numpy as np
-from tensorgraphx.data_iterator import SequentialIterator
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
+
 
 ## params
 x_dim = 50
@@ -35,15 +25,9 @@
 # define the graph model structure
 start = StartNode(input_vars=[x_ph])
 
-<<<<<<< HEAD
 h1 = HiddenNode(prev=[start], layers=[Linear(component_dim), Softmax()])
 h2 = HiddenNode(prev=[h1], layers=[Linear(component_dim), Softmax()])
 h3 = HiddenNode(prev=[h2], layers=[Linear(component_dim), Softmax()])
-=======
-h1 = HiddenNode(prev=[start], layers=[Linear(x_dim, component_dim), Softmax()])
-h2 = HiddenNode(prev=[h1], layers=[Linear(component_dim, component_dim), Softmax()])
-h3 = HiddenNode(prev=[h2], layers=[Linear(component_dim, component_dim), Softmax()])
->>>>>>> e55a706e1467da7b7c54b6d04055aba847f5a2b5
 
 
 e1 = EndNode(prev=[h1], input_merge_mode=Sum())