Create MNIST.py

Author: yanbin43

MNIST.py

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+#================================================================================================================#
+# Training and Validation
+#================================================================================================================#
+
+# Libraries needed:
+
+# - numpy (array)
+# - matplotlib (plotting)
+# - keras (deep learning) - image, convolution, maxpool, flatten, dense, models
+# - os (directory reading)
+
+# `image` = read images  
+# `model` = compile everything as a model so next time can use  
+# `flow_from_directory` = read image >> convert to array >> concatenate all images into big array
+
+#----------------------------------------------------------------------------------------------------------------#
+
+import numpy as np
+from matplotlib import pyplot as plt
+import os
+from keras.preprocessing import image
+from keras.layers import Conv2D, MaxPool2D, Flatten, Dense
+from keras.models import Model
+from keras.layers import Input # function of determining the input size in the first layer
+
+directory = '../input/mnistasjpg/trainingSet/trainingSet/'
+
+# image reader
+datagen = image.ImageDataGenerator(rescale = 1./255, validation_split = 0.2) 
+train_set = datagen.flow_from_directory(directory, target_size = (100, 100), batch_size = 16,
+                                        class_mode = 'categorical', subset = 'training')
+val_set = datagen.flow_from_directory(directory, target_size = (100, 100), batch_size = 16, 
+                                      class_mode = 'categorical', subset = 'validation')
+
+#----------------------------------------------------------------------------------------------------------------#
+
+def network(nb_class, inputsize): # nb_class = 10, input = (100, 100, 3)
+    input_img = Input(shape = inputsize)
+    x = Conv2D(16, (3,3), strides = (1,1), activation = 'relu', padding = 'same', name = 'gkm_conv1')(input_img) 
+        # 1st - number of filters, 2nd - filter size
+    x = MaxPool2D((3,3), strides = (2,2), padding = 'same', name = 'gkm_maxpool1')(x)
+    x = Conv2D(32, (3,3), strides = (1,1), activation = 'relu', padding = 'same', name = 'gkm_conv2')(x)
+    x = MaxPool2D((3,3), strides = (2,2), padding = 'same', name = 'gkm_maxpool2')(x)
+    x = Flatten(name = 'flatten')(x)
+    x = Dense(100, activation = 'relu')(x)
+    x = Dense(nb_class, activation = 'softmax')(x)
+    model = Model(input_img, x)
+    return model
+
+model = network(nb_class = 10, inputsize = (100,100,3))
+model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
+model.summary()
+
+# Save the model
+model.save('SimpleDL.h5')
+
+#----------------------------------------------------------------------------------------------------------------#
+
+# training and validation
+hist = model.fit(train_set, epochs = 20, steps_per_epoch = 25, validation_data = val_set, verbose = 1)
+
+# plot training loss and validation loss
+plt.plot(hist.history['loss'])
+plt.plot(hist.history['val_loss'])
+plt.legend(['training loss', 'validation loss'], loc = 'upper right')
+
+# plot training accuracy and validation accuracy
+plt.plot(hist.history['accuracy'])
+plt.plot(hist.history['val_accuracy'])
+plt.legend(['accuracy', 'validation accuracy'], loc = 'lower right')
+
+
+#================================================================================================================#
+# Testing
+#================================================================================================================#
+
+import cv2  # open cv (read images)
+
+# read the picture to be tested
+direct = '../input/mnistasjpg/testSet/testSet/img_10.jpg'
+img = cv2.imread(direct)
+img = cv2.resize(img, (100, 100))
+data = np.array(img)/255
+
+# DL trains in 4 dimensions, so data reshaping is needed
+data = data.reshape(1, 100, 100, 3)
+data.shape
+
+# Get the probability that this image is belong to each class
+result = model.predict(data)
+result.sum() # this is equal to 1
+
+# take the largest probability among 'result'
+output = np.argmax(result) 
+print(output)
+
+
+
+