'''Trains a simple convnet on the MNIST dataset.

Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function

import numpy as np

np.random.seed(1337)  # 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 keras import backend as K
from kerasmodel.files import *

batch_size = 128
nb_classes = 15
nb_epoch = 100

# input image dimensions
n_mel, n_frame = 128, 100
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
pool_size = (2, 2)
# convolution kernel size
kernel_size = (3, 3)

# the data, shuffled and split between train and test sets
# (X_train, y_train), (X_test, y_test) = mnist.load_data()

root='f100h0'
fold = str(1)
f_train=get_dump_name(root, fold, 'train')
X_train,y_train = load_data(f_train)
f_test=get_dump_name(root, fold, 'test')
X_test,y_test = load_data(f_test)

if K.image_dim_ordering() == 'th':
    X_train = X_train.reshape(X_train.shape[0], 1, n_mel, n_frame)
    X_test = X_test.reshape(X_test.shape[0], 1, n_mel, n_frame)
    input_shape = (1, n_mel, n_frame)
else:
    X_train = X_train.reshape(X_train.shape[0], n_mel, n_frame, 1)
    X_test = X_test.reshape(X_test.shape[0], n_mel, n_frame, 1)
    input_shape = (n_mel, n_frame, 1)

print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

model = Sequential()

model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
                        border_mode='valid',
                        input_shape=input_shape))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='adadelta',
              metrics=['accuracy'])

model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
          verbose=1, validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
