import numpy as np
np.random.seed(123)  # for reproducibility
 
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from dataset_pothole import pothole
from keras.models import model_from_json

# 4. Load pre-shuffled MNIST data into train and test sets
(X_train, y_train), (X_test, y_test) = pothole.load_data()
 
print(X_train.shape)
print()
print (y_train.shape)
print()

# 5. Preprocess input data
X_train = X_train.reshape(X_train.shape[0], 50, 50, 3)
X_test = X_test.reshape(X_test.shape[0], 50, 50, 3)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
 
# 6. Preprocess class labels
Y_train = np_utils.to_categorical(y_train, 2)
Y_test = np_utils.to_categorical(y_test, 2)
 
# 7. Define model architecture
model = Sequential()
model.add(Convolution2D(64, kernel_size=(3, 3), strides=(1, 1),
                 activation='relu',
                 input_shape=(50, 50, 3)))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

model.add(Flatten())
model.add(Dense(1000, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
 
# 8. Compile model
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
 
# 9. Fit model on training data
model.fit(X_train, Y_train, 
          batch_size=32, nb_epoch=10, verbose=1)
 
# 10. Evaluate model on test data
score = model.evaluate(X_test, Y_test, verbose=0)
# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
    json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")
print('Test loss: ', score[0])
print('Test accuracy: ', score[1])
