# from sklearn.metrics import classification_report

import os

import plaidml

from plaidml import keras

plaidml.keras.install_backend()

# import keras

import keras

from keras.models import Sequential

from keras.utils import Sequence

from keras.layers import Flatten, Dropout, Dense, BatchNormalization, Activation

from keras.layers import Conv2D, MaxPooling2D

from keras.optimizers import Adam

from keras.callbacks import LearningRateScheduler

from keras.preprocessing.image import ImageDataGenerator # when using notebook

import matplotlib

matplotlib.use("Agg")

os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"

import matplotlib.pyplot as plt

from scipy import ndimage

from imutils import paths

import config

import numpy as np

import argparse

ap = argparse.ArgumentParser()

ap.add_argument("-p", "--plot", type=str, default="plot.png",

help="path to output loss/accuracy plot")

args = vars(ap.parse_args())

NUM_EPOCHS = 80

INIT_LR = 1e-1

BS = 64

# def poly_decay(epoch):

# maxEpochs = NUM_EPOCHS

# baseLR = INIT_LR

# power = 1.0

# alpha = baseLR * (1 - (epoch / float(maxEpochs))) ** power

# return alpha

totalTrain = len(list(paths.list_images(config.TRAIN_PATH)))

print("[INFO] total training images {}".format(totalTrain))

totalVal = len(list(paths.list_images(config.VAL_PATH)))

print("[INFO] total validation images {}".format(totalVal))

totalTest = len(list(paths.list_images(config.TEST_PATH)))

print("[INFO] total testing images {}".format(totalTest))

trainAug = ImageDataGenerator(

)

valAug = ImageDataGenerator(rescale=1/255.0)

trainGen = trainAug.flow_from_directory(

)

valGen = valAug.flow_from_directory(

)

testGen = valAug.flow_from_directory(

)

# if os.path.exists('{}.meta'.format(MODEL_NAME)):

# model.load(MODEL_NAME)

# print('model loaded!')

# else:

model = Sequential()

model.add(Conv2D(32, kernel_size=(3, 3), padding='same', strides=(1, 1), activation='relu', input_shape=(64, 64, 3)))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, kernel_size=(5, 5), activation='relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dense(2, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,

optimizer=keras.optimizers.Adam(),

metrics=['accuracy']

)

print(model.summary())

# callbacks = [LearningRateScheduler(poly_decay)]

history = model.fit_generator(

)

print("[INFO] evaluating model...")

testGen.reset()

# predIdxs = model.predict_generator(testGen,

# steps=(totalTest//BS) + 1)

# predIdxs = np.argmax(predIdxs, axis=1)

score = model.evaluate_generator(testGen, verbose=1)

model.save("saved_model.model")

# Loss Curves

N = NUM_EPOCHS

plt.style.use("ggplot")

plt.figure()

plt.plot(np.arange(0, N), history.history["loss"], label="train_loss")

plt.plot(np.arange(0, N), history.history["val_loss"], label="val_loss")

plt.plot(np.arange(0, N), history.history["acc"], label="train_acc")

plt.plot(np.arange(0, N), history.history["val_acc"], label="val_acc")

plt.title("Training Loss and Accuracy on Dataset")

plt.xlabel("Epoch #")

plt.ylabel("Loss/Accuracy")

plt.legend(loc="lower left")

plt.savefig("Curves_Plot.png")

plt.show()

print("Test loss: {}".format(score[0]))

print("Test accuracy: {}".format(score[1]))