Neural networks learning
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import matplotlib.pyplot as plt
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import seaborn as sns
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import matplotlib.pyplot as plt
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import keras
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from keras.models import Sequential
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from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout
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from keras.preprocessing.image import ImageDataGenerator
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from keras.optimizers import Adam
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from sklearn.metrics import classification_report,confusion_matrix
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from tensorflow import keras
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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from tensorflow.keras import layers
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from sklearn.metrics import classification_report, confusion_matrix
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import tensorflow as tf
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@ -16,24 +17,151 @@ import os
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import numpy as np
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from resources.Globals import *
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labels = ['houses', 'other']
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img_size = 400
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def get_data(data_dir):
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data = []
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for label in labels:
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path = os.path.join(data_dir, label)
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class_num = labels.index(label)
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for img in os.listdir(path):
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try:
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img_arr = cv2.imread(os.path.join(path, img))[..., ::-1] # Convert BGR to RGB format
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resized_arr = cv2.resize(img_arr, (img_size, img_size)) # Reshaping images to preferred size
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data.append([resized_arr, class_num])
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except Exception as e:
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print(e)
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return np.array(data, dtype="object")
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def main():
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labels = ['house', 'other']
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img_size = 500
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train = get_data('E:/Projects/Pycharm Projects/sapper/files/Neural_networks/Train')
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val = get_data('E:/Projects/Pycharm Projects/sapper/files/Neural_networks/Test')
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def get_data(data_dir):
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data = []
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for label in labels:
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path = os.path.join(data_dir, label)
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class_num = labels.index(label)
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for img in os.listdir(path):
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try:
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img_arr = cv2.imread(os.path.join(path, img))[..., ::-1] # Convert BGR to RGB format
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resized_arr = cv2.resize(img_arr, (img_size, img_size)) # Reshaping images to preferred size
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data.append([resized_arr, class_num])
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except Exception as e:
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print(e)
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return np.array(data)
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# Visualize the data
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l = []
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for i in train:
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if i[1] != 0:
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l.append("houses")
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else:
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l.append("other")
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sns.set_style('darkgrid')
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sns.countplot(l)
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# House
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plt.figure(figsize=(5, 5))
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plt.imshow(train[1][0])
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plt.title(labels[train[0][1]])
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# Other
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plt.figure(figsize=(5, 5))
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plt.imshow(train[-1][0])
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plt.title(labels[train[-1][1]])
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# Data Preprocessing
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x_train = []
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y_train = []
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x_val = []
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y_val = []
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for feature, label in train:
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x_train.append(feature)
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y_train.append(label)
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for feature, label in val:
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x_val.append(feature)
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y_val.append(label)
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# Normalize the data
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x_train = np.array(x_train) / 255
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x_val = np.array(x_val) / 255
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x_train.reshape(-1, img_size, img_size, 1)
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y_train = np.array(y_train)
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x_val.reshape(-1, img_size, img_size, 1)
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y_val = np.array(y_val)
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# Data augmentation
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datagen = ImageDataGenerator(
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featurewise_center=False, # set input mean to 0 over the dataset
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samplewise_center=False, # set each sample mean to 0
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featurewise_std_normalization=False, # divide inputs by std of the dataset
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samplewise_std_normalization=False, # divide each input by its std
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zca_whitening=False, # apply ZCA whitening
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rotation_range=30, # randomly rotate images in the range (degrees, 0 to 180)
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zoom_range=0.2, # Randomly zoom image
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width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
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height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
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horizontal_flip=True, # randomly flip images
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vertical_flip=False) # randomly flip images
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print('Pretrain')
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datagen.fit(x_train)
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print('After train')
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# Define the Model
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model = Sequential()
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model.add(Conv2D(32, 3, padding="same", activation="relu", input_shape=(img_size, img_size, 3)))
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model.add(MaxPool2D())
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model.add(Conv2D(32, 3, padding="same", activation="relu"))
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model.add(MaxPool2D())
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model.add(Conv2D(64, 3, padding="same", activation="relu"))
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model.add(MaxPool2D())
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model.add(Dropout(0.4))
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model.add(Flatten())
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model.add(Dense(128, activation="relu"))
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model.add(Dense(2, activation="softmax"))
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model.summary()
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# Compile the model
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opt = keras.optimizers.Adam(lr=0.000001) # Adam as optimizer and SparseCategoricalCrossentropy as the loss function
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model.compile(optimizer=opt, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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metrics=['accuracy'])
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print('Pretrain #2')
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# Train model
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history = model.fit(x_train, y_train, epochs=AMOUNT_OF_EPOCHS, validation_data=(x_val, y_val))
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print('After train #2')
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# Evaluating the result
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acc = history.history['accuracy']
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val_acc = history.history['val_accuracy']
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loss = history.history['loss']
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val_loss = history.history['val_loss']
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epochs_range = range(1, AMOUNT_OF_EPOCHS + 1)
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plt.figure(figsize=(15, 15))
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plt.subplot(2, 2, 1)
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plt.plot(epochs_range, acc, label='Training Accuracy')
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plt.plot(epochs_range, val_acc, label='Validation Accuracy')
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plt.legend(loc='lower right')
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plt.title('Training and Validation Accuracy')
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plt.subplot(2, 2, 2)
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plt.plot(epochs_range, loss, label='Training Loss')
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plt.plot(epochs_range, val_loss, label='Validation Loss')
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plt.legend(loc='upper right')
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plt.title('Training and Validation Loss')
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# Precision and accuracy report
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predictions = model.predict_classes(x_val)
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predictions = predictions.reshape(1, -1)[0]
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print(classification_report(y_val, predictions, target_names=['Rugby (Class 0)', 'Soccer (Class 1)']))
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# Show all plots
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plt.show()
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if __name__ == '__main__':
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@ -1,15 +1,2 @@
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import pandas as pd
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def main():
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my_dict = {'known': [1], 'power': [1], 'new': [1], 'location': 0, 'stable': [1], 'chain_reaction': [1]}
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s = pd.DataFrame.from_dict(my_dict)
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print(s)
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# data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
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# print(pd.DataFrame.from_dict(data))
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main()
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t = range(10)
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print(t)
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