from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import numpy as np
import sklearn.metrics as metrics

from keras import backend as K
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.datasets import cifar10
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils
from keras_contrib.applications.densenet import DenseNet


batch_size = 64
nb_classes = 10
nb_epoch = 300

img_rows, img_cols = 32, 32
img_channels = 3

# Parameters for the DenseNet model builder
img_dim = (img_channels, img_rows, img_cols) if K.image_dim_ordering() == "th" else (img_rows, img_cols, img_channels)
depth = 40
nb_dense_block = 3
growth_rate = 12
nb_filter = 16
dropout_rate = 0.0 # 0.0 for data augmentation

# Create the model (without loading weights)
model = DenseNet(depth, nb_dense_block, growth_rate, nb_filter, dropout_rate=dropout_rate,
                 input_shape=img_dim, weights=None)
print("Model created")

model.summary()

optimizer = Adam(lr=1e-3) # Using Adam instead of SGD to speed up training
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=["accuracy"])
print("Finished compiling")

(trainX, trainY), (testX, testY) = cifar10.load_data()

trainX = trainX.astype('float32')
testX = testX.astype('float32')

trainX /= 255.
testX /= 255.

Y_train = np_utils.to_categorical(trainY, nb_classes)
Y_test = np_utils.to_categorical(testY, nb_classes)

generator = ImageDataGenerator(rotation_range=15,
                               width_shift_range=5./32,
                               height_shift_range=5./32)

generator.fit(trainX, seed=0)

weights_file = "DenseNet-40-12-CIFAR-10.h5"

lr_reducer      = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), 
                                    cooldown=0, patience=10, min_lr=0.5e-6)
early_stopper   = EarlyStopping(monitor='val_acc', min_delta=1e-4, patience=20)
model_checkpoint= ModelCheckpoint(weights_file, monitor="val_acc", save_best_only=True,
                                  save_weights_only=True,mode='auto')

callbacks = [lr_reducer, early_stopper, model_checkpoint]

model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size), samples_per_epoch=len(trainX), nb_epoch=nb_epoch,
                   callbacks=callbacks,
                   validation_data=(testX, Y_test),
                   nb_val_samples=testX.shape[0], verbose=2)

yPreds = model.predict(testX)
yPred = np.argmax(yPreds, axis=1)
print(yPred)
yTrue = testY

accuracy = metrics.accuracy_score(yTrue, yPred) * 100
error = 100 - accuracy
print("Accuracy : ", accuracy)
print("Error : ", error)