from sklearn.externals import joblib
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
import numpy as np
import pandas as pd
import os
#changes working directory
os.chdir("D:\\Kartheek\\DigitRecog")
(X_train,y_train),(X_test,y_test)=mnist.load_data()
x_train=X_train.reshape(X_train.shape[0],28,28,1).astype('float32')
x_test=X_test.reshape(X_test.shape[0],28,28,1).astype('float32')
print(X_train.shape)
print(X_test.shape)
#os.chdir("D:\\NeuralNetwork")
batch_size = 132
num_classes = 10
epochs = 16
# input image dimensions
img_rows, img_cols = 28, 28
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)
#cv2.imwrite('messigray.png',x_train[0])
model = Sequential()
model.add(Conv2D(64, kernel_size=(5, 5),
activation='relu',
input_shape=(28,28,1)))
model.add(Conv2D(128, (5, 5), 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(0.25))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
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])
Images = pd.read_csv("test.csv")
Images = Images.values
Images.shape
x_predict=Images.reshape(28000,28,28,1)
y_predict=model.predict(x_predict)
Submission = pd.read_csv("submission.csv")
z=y_predict.argmax(axis=1)
Submission["Label"]=z
Submission.to_csv("submission.csv",index=False)
joblib.dump(model, "digits_cls.pkl", compress=3)
Images = pd.read_csv("test.csv")
Images.shape
Images = Images.values
Images.shape
x_predict=Images.reshape(28000,28,28,1)
y_predict=model.predict(x_predict)
Submission = pd.read_csv("submission.csv")
z=y_predict.argmax(axis=1)
Submission["Label"]=z
Submission.to_csv("submission.csv",index=False)
