from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras import backend as K
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd

seed = 785
np.random.seed(seed)

dataset = np.loadtxt('../input/A_Z Handwritten Data/A_Z Handwritten Data.csv', delimiter=',')

print(dataset.shape) # (372451, 785)

X = dataset[:,1:785]
Y = dataset[:,0]

(X_train, X_test, Y_train, Y_test) = train_test_split(X, Y, test_size=0.33, random_state=seed)

X_train = X_train / 255
X_test = X_test / 255

X_train.reshape([-1, X_train.shape[0], X_train.shape[1], 1])
X_test.reshape([-1, X_test.shape[0], X_test.shape[1], 1])

Y_train = np_utils.to_categorical(Y_train)
Y_test = np_utils.to_categorical(Y_test)

print(Y_test.shape) # (122909, 26)

num_classes = Y_test.shape[1] # 26

model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu', data_format="channels_last"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print("DONE")
model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=10, batch_size=256, verbose=2) # WHERE I GET THE ERROR


# Final evaluation of the model
scores = model.evaluate(X_test,Y_test, verbose=0)
print("CNN Error: %.2f%%" % (100-scores[1]*100))

model.save('weights.model')