More examples

Author: ahmedfgad

Tutorial Project/README.md

@@ -22,7 +22,7 @@
 author = 'Ahmed Fawzy Gad'
 
 # The full version, including alpha/beta/rc tags
-release = '3.0.1'
+release = '3.1.0'
 
 master_doc = 'index'
 

docs/source/conf.py

@@ -0,0 +1,72 @@
+import numpy
+import pygad.cnn
+
+"""
+Convolutional neural network implementation using NumPy
+A tutorial that helps to get started (Building Convolutional Neural Network using NumPy from Scratch) available in these links: 
+    https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad
+    https://towardsdatascience.com/building-convolutional-neural-network-using-numpy-from-scratch-b30aac50e50a
+    https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html
+It is also translated into Chinese: http://m.aliyun.com/yunqi/articles/585741
+"""
+
+train_inputs = numpy.load("../data/dataset_inputs.npy")
+train_outputs = numpy.load("../data/dataset_outputs.npy")
+
+sample_shape = train_inputs.shape[1:]
+num_classes = 4
+
+input_layer = pygad.cnn.Input2D(input_shape=sample_shape)
+conv_layer1 = pygad.cnn.Conv2D(num_filters=2,
+                               kernel_size=3,
+                               previous_layer=input_layer,
+                               activation_function=None)
+relu_layer1 = pygad.cnn.Sigmoid(previous_layer=conv_layer1)
+average_pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2, 
+                                                   previous_layer=relu_layer1,
+                                                   stride=2)
+
+conv_layer2 = pygad.cnn.Conv2D(num_filters=3,
+                               kernel_size=3,
+                               previous_layer=average_pooling_layer,
+                               activation_function=None)
+relu_layer2 = pygad.cnn.ReLU(previous_layer=conv_layer2)
+max_pooling_layer = pygad.cnn.MaxPooling2D(pool_size=2, 
+                                           previous_layer=relu_layer2,
+                                           stride=2)
+
+conv_layer3 = pygad.cnn.Conv2D(num_filters=1,
+                               kernel_size=3,
+                               previous_layer=max_pooling_layer,
+                               activation_function=None)
+relu_layer3 = pygad.cnn.ReLU(previous_layer=conv_layer3)
+pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2, 
+                                           previous_layer=relu_layer3,
+                                           stride=2)
+
+flatten_layer = pygad.cnn.Flatten(previous_layer=pooling_layer)
+dense_layer1 = pygad.cnn.Dense(num_neurons=100, 
+                               previous_layer=flatten_layer,
+                               activation_function="relu")
+dense_layer2 = pygad.cnn.Dense(num_neurons=num_classes, 
+                               previous_layer=dense_layer1,
+                               activation_function="softmax")
+
+model = pygad.cnn.Model(last_layer=dense_layer2,
+                        epochs=1,
+                        learning_rate=0.01)
+
+model.summary()
+
+model.train(train_inputs=train_inputs, 
+            train_outputs=train_outputs)
+
+predictions = model.predict(data_inputs=train_inputs)
+print(predictions)
+
+num_wrong = numpy.where(predictions != train_outputs)[0]
+num_correct = train_outputs.size - num_wrong.size
+accuracy = 100 * (num_correct/train_outputs.size)
+print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct))
+print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size))
+print("Classification accuracy : {accuracy}.".format(accuracy=accuracy))

docs/source/index.rst

@@ -0,0 +1,160 @@
+Species,Weight,Length1,Length2,Length3,Height,Width
+Bream,242,23.2,25.4,30,11.52,4.02
+Bream,290,24,26.3,31.2,12.48,4.3056
+Bream,340,23.9,26.5,31.1,12.3778,4.6961
+Bream,363,26.3,29,33.5,12.73,4.4555
+Bream,430,26.5,29,34,12.444,5.134
+Bream,450,26.8,29.7,34.7,13.6024,4.9274
+Bream,500,26.8,29.7,34.5,14.1795,5.2785
+Bream,390,27.6,30,35,12.67,4.69
+Bream,450,27.6,30,35.1,14.0049,4.8438
+Bream,500,28.5,30.7,36.2,14.2266,4.9594
+Bream,475,28.4,31,36.2,14.2628,5.1042
+Bream,500,28.7,31,36.2,14.3714,4.8146
+Bream,500,29.1,31.5,36.4,13.7592,4.368
+Bream,340,29.5,32,37.3,13.9129,5.0728
+Bream,600,29.4,32,37.2,14.9544,5.1708
+Bream,600,29.4,32,37.2,15.438,5.58
+Bream,700,30.4,33,38.3,14.8604,5.2854
+Bream,700,30.4,33,38.5,14.938,5.1975
+Bream,610,30.9,33.5,38.6,15.633,5.1338
+Bream,650,31,33.5,38.7,14.4738,5.7276
+Bream,575,31.3,34,39.5,15.1285,5.5695
+Bream,685,31.4,34,39.2,15.9936,5.3704
+Bream,620,31.5,34.5,39.7,15.5227,5.2801
+Bream,680,31.8,35,40.6,15.4686,6.1306
+Bream,700,31.9,35,40.5,16.2405,5.589
+Bream,725,31.8,35,40.9,16.36,6.0532
+Bream,720,32,35,40.6,16.3618,6.09
+Bream,714,32.7,36,41.5,16.517,5.8515
+Bream,850,32.8,36,41.6,16.8896,6.1984
+Bream,1000,33.5,37,42.6,18.957,6.603
+Bream,920,35,38.5,44.1,18.0369,6.3063
+Bream,955,35,38.5,44,18.084,6.292
+Bream,925,36.2,39.5,45.3,18.7542,6.7497
+Bream,975,37.4,41,45.9,18.6354,6.7473
+Bream,950,38,41,46.5,17.6235,6.3705
+Roach,40,12.9,14.1,16.2,4.1472,2.268
+Roach,69,16.5,18.2,20.3,5.2983,2.8217
+Roach,78,17.5,18.8,21.2,5.5756,2.9044
+Roach,87,18.2,19.8,22.2,5.6166,3.1746
+Roach,120,18.6,20,22.2,6.216,3.5742
+Roach,0,19,20.5,22.8,6.4752,3.3516
+Roach,110,19.1,20.8,23.1,6.1677,3.3957
+Roach,120,19.4,21,23.7,6.1146,3.2943
+Roach,150,20.4,22,24.7,5.8045,3.7544
+Roach,145,20.5,22,24.3,6.6339,3.5478
+Roach,160,20.5,22.5,25.3,7.0334,3.8203
+Roach,140,21,22.5,25,6.55,3.325
+Roach,160,21.1,22.5,25,6.4,3.8
+Roach,169,22,24,27.2,7.5344,3.8352
+Roach,161,22,23.4,26.7,6.9153,3.6312
+Roach,200,22.1,23.5,26.8,7.3968,4.1272
+Roach,180,23.6,25.2,27.9,7.0866,3.906
+Roach,290,24,26,29.2,8.8768,4.4968
+Roach,272,25,27,30.6,8.568,4.7736
+Roach,390,29.5,31.7,35,9.485,5.355
+Whitefish,270,23.6,26,28.7,8.3804,4.2476
+Whitefish,270,24.1,26.5,29.3,8.1454,4.2485
+Whitefish,306,25.6,28,30.8,8.778,4.6816
+Whitefish,540,28.5,31,34,10.744,6.562
+Whitefish,800,33.7,36.4,39.6,11.7612,6.5736
+Whitefish,1000,37.3,40,43.5,12.354,6.525
+Parkki,55,13.5,14.7,16.5,6.8475,2.3265
+Parkki,60,14.3,15.5,17.4,6.5772,2.3142
+Parkki,90,16.3,17.7,19.8,7.4052,2.673
+Parkki,120,17.5,19,21.3,8.3922,2.9181
+Parkki,150,18.4,20,22.4,8.8928,3.2928
+Parkki,140,19,20.7,23.2,8.5376,3.2944
+Parkki,170,19,20.7,23.2,9.396,3.4104
+Parkki,145,19.8,21.5,24.1,9.7364,3.1571
+Parkki,200,21.2,23,25.8,10.3458,3.6636
+Parkki,273,23,25,28,11.088,4.144
+Parkki,300,24,26,29,11.368,4.234
+Perch,5.9,7.5,8.4,8.8,2.112,1.408
+Perch,32,12.5,13.7,14.7,3.528,1.9992
+Perch,40,13.8,15,16,3.824,2.432
+Perch,51.5,15,16.2,17.2,4.5924,2.6316
+Perch,70,15.7,17.4,18.5,4.588,2.9415
+Perch,100,16.2,18,19.2,5.2224,3.3216
+Perch,78,16.8,18.7,19.4,5.1992,3.1234
+Perch,80,17.2,19,20.2,5.6358,3.0502
+Perch,85,17.8,19.6,20.8,5.1376,3.0368
+Perch,85,18.2,20,21,5.082,2.772
+Perch,110,19,21,22.5,5.6925,3.555
+Perch,115,19,21,22.5,5.9175,3.3075
+Perch,125,19,21,22.5,5.6925,3.6675
+Perch,130,19.3,21.3,22.8,6.384,3.534
+Perch,120,20,22,23.5,6.11,3.4075
+Perch,120,20,22,23.5,5.64,3.525
+Perch,130,20,22,23.5,6.11,3.525
+Perch,135,20,22,23.5,5.875,3.525
+Perch,110,20,22,23.5,5.5225,3.995
+Perch,130,20.5,22.5,24,5.856,3.624
+Perch,150,20.5,22.5,24,6.792,3.624
+Perch,145,20.7,22.7,24.2,5.9532,3.63
+Perch,150,21,23,24.5,5.2185,3.626
+Perch,170,21.5,23.5,25,6.275,3.725
+Perch,225,22,24,25.5,7.293,3.723
+Perch,145,22,24,25.5,6.375,3.825
+Perch,188,22.6,24.6,26.2,6.7334,4.1658
+Perch,180,23,25,26.5,6.4395,3.6835
+Perch,197,23.5,25.6,27,6.561,4.239
+Perch,218,25,26.5,28,7.168,4.144
+Perch,300,25.2,27.3,28.7,8.323,5.1373
+Perch,260,25.4,27.5,28.9,7.1672,4.335
+Perch,265,25.4,27.5,28.9,7.0516,4.335
+Perch,250,25.4,27.5,28.9,7.2828,4.5662
+Perch,250,25.9,28,29.4,7.8204,4.2042
+Perch,300,26.9,28.7,30.1,7.5852,4.6354
+Perch,320,27.8,30,31.6,7.6156,4.7716
+Perch,514,30.5,32.8,34,10.03,6.018
+Perch,556,32,34.5,36.5,10.2565,6.3875
+Perch,840,32.5,35,37.3,11.4884,7.7957
+Perch,685,34,36.5,39,10.881,6.864
+Perch,700,34,36,38.3,10.6091,6.7408
+Perch,700,34.5,37,39.4,10.835,6.2646
+Perch,690,34.6,37,39.3,10.5717,6.3666
+Perch,900,36.5,39,41.4,11.1366,7.4934
+Perch,650,36.5,39,41.4,11.1366,6.003
+Perch,820,36.6,39,41.3,12.4313,7.3514
+Perch,850,36.9,40,42.3,11.9286,7.1064
+Perch,900,37,40,42.5,11.73,7.225
+Perch,1015,37,40,42.4,12.3808,7.4624
+Perch,820,37.1,40,42.5,11.135,6.63
+Perch,1100,39,42,44.6,12.8002,6.8684
+Perch,1000,39.8,43,45.2,11.9328,7.2772
+Perch,1100,40.1,43,45.5,12.5125,7.4165
+Perch,1000,40.2,43.5,46,12.604,8.142
+Perch,1000,41.1,44,46.6,12.4888,7.5958
+Pike,200,30,32.3,34.8,5.568,3.3756
+Pike,300,31.7,34,37.8,5.7078,4.158
+Pike,300,32.7,35,38.8,5.9364,4.3844
+Pike,300,34.8,37.3,39.8,6.2884,4.0198
+Pike,430,35.5,38,40.5,7.29,4.5765
+Pike,345,36,38.5,41,6.396,3.977
+Pike,456,40,42.5,45.5,7.28,4.3225
+Pike,510,40,42.5,45.5,6.825,4.459
+Pike,540,40.1,43,45.8,7.786,5.1296
+Pike,500,42,45,48,6.96,4.896
+Pike,567,43.2,46,48.7,7.792,4.87
+Pike,770,44.8,48,51.2,7.68,5.376
+Pike,950,48.3,51.7,55.1,8.9262,6.1712
+Pike,1250,52,56,59.7,10.6863,6.9849
+Pike,1600,56,60,64,9.6,6.144
+Pike,1550,56,60,64,9.6,6.144
+Pike,1650,59,63.4,68,10.812,7.48
+Smelt,6.7,9.3,9.8,10.8,1.7388,1.0476
+Smelt,7.5,10,10.5,11.6,1.972,1.16
+Smelt,7,10.1,10.6,11.6,1.7284,1.1484
+Smelt,9.7,10.4,11,12,2.196,1.38
+Smelt,9.8,10.7,11.2,12.4,2.0832,1.2772
+Smelt,8.7,10.8,11.3,12.6,1.9782,1.2852
+Smelt,10,11.3,11.8,13.1,2.2139,1.2838
+Smelt,9.9,11.3,11.8,13.1,2.2139,1.1659
+Smelt,9.8,11.4,12,13.2,2.2044,1.1484
+Smelt,12.2,11.5,12.2,13.4,2.0904,1.3936
+Smelt,13.4,11.7,12.4,13.5,2.43,1.269
+Smelt,12.2,12.1,13,13.8,2.277,1.2558
+Smelt,19.7,13.2,14.3,15.2,2.8728,2.0672
+Smelt,19.9,13.8,15,16.2,2.9322,1.8792