Neural networks learning

This commit is contained in:
Andrzej 2021-06-19 23:20:17 +02:00
parent efda9dd3e9
commit ea4e306c63
1876 changed files with 154 additions and 37 deletions

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@ -1,13 +1,14 @@
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential
from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam
from sklearn.metrics import classification_report,confusion_matrix
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import layers
from sklearn.metrics import classification_report, confusion_matrix
import tensorflow as tf
@ -16,24 +17,151 @@ import os
import numpy as np
from resources.Globals import *
labels = ['houses', 'other']
img_size = 400
def get_data(data_dir):
data = []
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
try:
img_arr = cv2.imread(os.path.join(path, img))[..., ::-1] # Convert BGR to RGB format
resized_arr = cv2.resize(img_arr, (img_size, img_size)) # Reshaping images to preferred size
data.append([resized_arr, class_num])
except Exception as e:
print(e)
return np.array(data, dtype="object")
def main():
labels = ['house', 'other']
img_size = 500
train = get_data('E:/Projects/Pycharm Projects/sapper/files/Neural_networks/Train')
val = get_data('E:/Projects/Pycharm Projects/sapper/files/Neural_networks/Test')
def get_data(data_dir):
data = []
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
try:
img_arr = cv2.imread(os.path.join(path, img))[..., ::-1] # Convert BGR to RGB format
resized_arr = cv2.resize(img_arr, (img_size, img_size)) # Reshaping images to preferred size
data.append([resized_arr, class_num])
except Exception as e:
print(e)
return np.array(data)
# 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__':

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import pandas as pd
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()
t = range(10)
print(t)

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