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prepare_train_model.py

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+# Source: https://www.guru99.com/keras-tutorial.html
+
+#### Data preparation
+from keras.preprocessing.image import ImageDataGenerator
+import numpy as np
+import matplotlib.pyplot as plt
+
+train_path = 'images/train/'
+test_path = 'images/test/'
+batch_size = 16
+image_size = 224
+num_class = 8
+
+'''
+The ImageDataGenerator will make an X_training data from a directory. 
+The sub-directory in that directory will be used as a class for each object. 
+The image will be loaded with the RGB color mode, with the categorical class 
+mode for the Y_training data, with a batch size of 16. Finally, shuffle the 
+data.
+'''
+train_datagen = ImageDataGenerator(validation_split=0.3,
+                                   shear_range=0.2,
+                                   zoom_range=0.2,
+                                   horizontal_flip=True)
+
+train_generator = train_datagen.flow_from_directory(
+                        directory=train_path,
+                        target_size=(image_size,image_size),
+                        batch_size=batch_size,
+                        class_mode='categorical',
+                        color_mode='rgb',
+                        shuffle=True)
+
+'''
+Let's see our images randomly by plotting them with matplotlib
+'''
+x_batch, y_batch = train_generator.next()
+fig=plt.figure()
+columns = 4
+rows = 4
+for i in range(1, columns*rows):
+    num = np.random.randint(batch_size)
+    image = x_batch[num].astype(np.int)
+    fig.add_subplot(rows, columns, i)
+    plt.imshow(image)
+plt.show()
+
+### Creating model
+import keras
+from keras.models import Model, load_model
+from keras.layers import Activation, Dropout, Flatten, Dense
+from keras.preprocessing.image import ImageDataGenerator
+from keras.applications.vgg16 import VGG16
+
+'''
+Let's create our network model from VGG16 with imageNet pre-trained weight. 
+We will freeze these layers so that the layers are not trainable to help us 
+reduce the computation time.
+'''
+
+# Load the VGG model
+base_model = VGG16(weights='imagenet', include_top=False, input_shape=(image_size, image_size, 3))
+
+print(base_model.summary())
+
+# Freeze the layers 
+for layer in base_model.layers:
+    layer.trainable = False
+ 
+# Create the model
+model = keras.models.Sequential()
+
+# Add the vgg convolutional base model
+model.add(base_model)
+ 
+# Add new layers
+model.add(Flatten())
+model.add(Dense(1024, activation='relu'))
+model.add(Dense(1024, activation='relu'))
+model.add(Dense(num_class, activation='softmax'))
+ 
+# Show a summary of the model. Check the number of trainable parameters    
+print(model.summary())
+
+input("Press Enter to continue...")
+
+'''
+As you can see, the summary of our network model. From an input from 
+VGG16 Layers, then we add 2 Fully Connected Layer which will extract 1024 
+features and an output layer that will compute the 8 classes with the softmax 
+activation.
+'''
+
+#### Training 
+# Compile the model
+from keras.optimizers import SGD
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=SGD(lr=1e-3),
+              metrics=['accuracy'])
+
+'''
+# Start the training process
+model.fit(x_train, y_train, validation_split=0.30, batch_size=32, epochs=50, verbose=2)
+
+# Save the model
+model.save('catdog.h5')
+'''
+
+history = model.fit_generator(train_generator,
+                              steps_per_epoch=train_generator.n/batch_size,
+                              epochs=10)
+        
+model.save('fine_tune.h5')
+
+# summarize history for accuracy
+import matplotlib.pyplot as plt
+
+plt.plot(history.history['loss'])
+plt.title('loss')
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(['loss'], loc='upper left')
+plt.show()
+
+'''
+As you can see, our losses are dropped significantly and the accuracy is 
+almost 100%. For testing our model, we randomly picked images over the internet 
+and put it on the test folder with a different class to test
+'''