Renamed VGG16 files to snake naming style

vgg16_prepare_train_model.py

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+"""
+Simple example on how to fine tune the VGG16 model in Keras and how to use them.
+Part 1
+
+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
+import keras
+from keras.layers import Flatten, Dense
+from keras.applications.vgg16 import VGG16
+from keras.optimizers import SGD
+
+TRAIN_PATH  = 'images/train/'
+TEST_PATH   = 'images/test/'
+BATCH_SIZE  = 16
+IMAGE_SIZE  = 224
+NUM_CLASSES = 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()
+x_batch, y_batch = next(train_generator)
+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
+# 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_CLASSES, 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
+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
+plt.plot(history.history['loss'])
+plt.title('loss')
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(['loss'], loc='upper left')
+plt.show()
+
+# 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