model_trainer.py

from load_images import load
import random
import numpy as np
from model_architecture import Model
from sklearn.model_selection import train_test_split
from keras.utils import to_categorical
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam

epochs = 25
learning_rate = 0.001
bs = 32
data = []
labels = []
architecture = Model(64, 64, 3, 3)
model = architecture.model3()
model.summary()
ld = load(64, 64, 3, [['D:/blind project 2/left'], ['D:/blind project 2/right'], ['D:/blind project 2/center']])

data, labels = ld.imgload()
# print(data)
c = list(zip(data, labels))
random.shuffle(c)
data[:], labels[:] = zip(*c)
data = np.array(data, dtype="float")/255.0
labels = np.array(labels)
trainX, testX, trainY, testY = train_test_split(data, labels, test_size=0.1, random_state=42)
trainY = to_categorical(trainY, num_classes=3)
testY = to_categorical(testY, num_classes=3)
aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.2,
                         zoom_range=0.2, fill_mode="nearest")
opt = Adam(lr=learning_rate, decay=learning_rate/epochs)
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
h = model.fit_generator(aug.flow(trainX, trainY, batch_size=bs), validation_data=(testX, testY),
                        steps_per_epoch=len(trainX)//bs, epochs=epochs, verbose=1)
model.save("blind_with_regularization.model")