'''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 keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import utill
import os
import numpy as np


batch_size = 128
num_classes = 2
epochs = 12

# input image dimensions
img_rows, img_cols = 128, 87 # Needs to be set to the dimensions of the Nsynth Data


cwd = os.getcwd()
# the data, split between train and test sets
bass_set=utill.load_npz_old(cwd+'\\Keras\\nsynth_melSpecs\\bas_1000.npz')

voc_set=utill.load_npz_old(cwd+'\\Keras\\nsynth_melSpecs\\voc_1000.npz')
X = np.append(bass_set,voc_set, axis=0)
y = np.append(np.ones(1000,dtype=int),np.zeros(1000,dtype=int))

x_train, x_test, y_train, y_test = \
        train_test_split(X, y, test_size=.4, random_state=42)

# if K.image_data_format() == 'channels_first':
#     x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
#     x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
#     input_shape = (1, img_rows, img_cols)
# else:
#     x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
#     x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
#     input_shape = (img_rows, img_cols, 1)

x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

print(y_train.shape)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255 # Test with this and without
x_test /= 255 # Test with this and without
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 = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
print(y_test.shape)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

history=model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])


import matplotlib.pyplot as plt
print('Plotting')
# Plotting of accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show(block = False)
# Plotting of loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show(block = False)
input('press <ENTER> to continue')
