Neural networks learning

bin/Main/NeuralNetwork.py

@@ -1,11 +1,12 @@
-import matplotlib.pyplot as plt
 import seaborn as sns
+import matplotlib.pyplot as plt
 
-import keras
+
-from keras.models import Sequential
+from tensorflow import keras
-from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout
+from tensorflow.keras.models import Sequential
-from keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout
-from keras.optimizers import Adam
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras import layers
 
 from sklearn.metrics import classification_report, confusion_matrix
 
@@ -16,10 +17,11 @@ import os
 
 import numpy as np
 
+from resources.Globals import *
+
+labels = ['houses', 'other']
+img_size = 400
 
-def main():
-    labels = ['house', 'other']
-    img_size = 500
 
 def get_data(data_dir):
     data = []
@@ -33,7 +35,133 @@ def main():
                 data.append([resized_arr, class_num])
             except Exception as e:
                 print(e)
-        return np.array(data)
+    return np.array(data, dtype="object")
+
+
+def main():
+    train = get_data('E:/Projects/Pycharm Projects/sapper/files/Neural_networks/Train')
+    val = get_data('E:/Projects/Pycharm Projects/sapper/files/Neural_networks/Test')
+
+    # Visualize the data
+    l = []
+    for i in train:
+        if i[1] != 0:
+            l.append("houses")
+        else:
+            l.append("other")
+    sns.set_style('darkgrid')
+    sns.countplot(l)
+
+    # House
+    plt.figure(figsize=(5, 5))
+    plt.imshow(train[1][0])
+    plt.title(labels[train[0][1]])
+
+    # Other
+    plt.figure(figsize=(5, 5))
+    plt.imshow(train[-1][0])
+    plt.title(labels[train[-1][1]])
+
+    # Data Preprocessing
+    x_train = []
+    y_train = []
+    x_val = []
+    y_val = []
+
+    for feature, label in train:
+        x_train.append(feature)
+        y_train.append(label)
+
+    for feature, label in val:
+        x_val.append(feature)
+        y_val.append(label)
+
+    # Normalize the data
+    x_train = np.array(x_train) / 255
+    x_val = np.array(x_val) / 255
+
+    x_train.reshape(-1, img_size, img_size, 1)
+    y_train = np.array(y_train)
+
+    x_val.reshape(-1, img_size, img_size, 1)
+    y_val = np.array(y_val)
+
+    # Data augmentation
+    datagen = ImageDataGenerator(
+        featurewise_center=False,  # set input mean to 0 over the dataset
+        samplewise_center=False,  # set each sample mean to 0
+        featurewise_std_normalization=False,  # divide inputs by std of the dataset
+        samplewise_std_normalization=False,  # divide each input by its std
+        zca_whitening=False,  # apply ZCA whitening
+        rotation_range=30,  # randomly rotate images in the range (degrees, 0 to 180)
+        zoom_range=0.2,  # Randomly zoom image
+        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
+        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
+        horizontal_flip=True,  # randomly flip images
+        vertical_flip=False)  # randomly flip images
+
+    print('Pretrain')
+
+    datagen.fit(x_train)
+
+    print('After train')
+
+    # Define the Model
+    model = Sequential()
+    model.add(Conv2D(32, 3, padding="same", activation="relu", input_shape=(img_size, img_size, 3)))
+    model.add(MaxPool2D())
+
+    model.add(Conv2D(32, 3, padding="same", activation="relu"))
+    model.add(MaxPool2D())
+
+    model.add(Conv2D(64, 3, padding="same", activation="relu"))
+    model.add(MaxPool2D())
+    model.add(Dropout(0.4))
+
+    model.add(Flatten())
+    model.add(Dense(128, activation="relu"))
+    model.add(Dense(2, activation="softmax"))
+
+    model.summary()
+
+    # Compile the model
+    opt = keras.optimizers.Adam(lr=0.000001)     # Adam as optimizer and SparseCategoricalCrossentropy as the loss function
+    model.compile(optimizer=opt, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+                  metrics=['accuracy'])
+
+    print('Pretrain #2')
+    # Train model
+    history = model.fit(x_train, y_train, epochs=AMOUNT_OF_EPOCHS, validation_data=(x_val, y_val))
+    print('After train #2')
+
+    # Evaluating the result
+    acc = history.history['accuracy']
+    val_acc = history.history['val_accuracy']
+    loss = history.history['loss']
+    val_loss = history.history['val_loss']
+
+    epochs_range = range(1, AMOUNT_OF_EPOCHS + 1)
+
+    plt.figure(figsize=(15, 15))
+    plt.subplot(2, 2, 1)
+    plt.plot(epochs_range, acc, label='Training Accuracy')
+    plt.plot(epochs_range, val_acc, label='Validation Accuracy')
+    plt.legend(loc='lower right')
+    plt.title('Training and Validation Accuracy')
+
+    plt.subplot(2, 2, 2)
+    plt.plot(epochs_range, loss, label='Training Loss')
+    plt.plot(epochs_range, val_loss, label='Validation Loss')
+    plt.legend(loc='upper right')
+    plt.title('Training and Validation Loss')
+
+    # Precision and accuracy report
+    predictions = model.predict_classes(x_val)
+    predictions = predictions.reshape(1, -1)[0]
+    print(classification_report(y_val, predictions, target_names=['Rugby (Class 0)', 'Soccer (Class 1)']))
+
+    # Show all plots
+    plt.show()
 
 
 if __name__ == '__main__':

bin/Main/test.py

@@ -1,15 +1,2 @@
-import pandas as pd
+t = range(10)
-
+print(t)
-
-def main():
-    my_dict = {'known': [1], 'power': [1], 'new': [1], 'location': 0, 'stable': [1], 'chain_reaction': [1]}
-
-    s = pd.DataFrame.from_dict(my_dict)
-
-    print(s)
-
-    # data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
-    # print(pd.DataFrame.from_dict(data))
-
-
-main()