cnn_numtadb(all)_dataset.py

# -*- coding: utf-8 -*-
"""CNN-Numtadb(All)_Dataset.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1l6gFx1tr9m4T1WW-P55v5FmRixLku775
"""

import cv2
import keras
import numpy as np
import pandas as pd
from keras import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dropout, Dense

x = np.load('/content/drive/My Drive/LICT training/Mosfiqun Nahid Hassan/Preprocessing_Data( Jupyter)/Numta_x.npy')/255.0
y = np.load('/content/drive/My Drive/LICT training/Mosfiqun Nahid Hassan/Preprocessing_Data( Jupyter)/Numta_y.npy')

x = x.reshape(-1,28,28,1)
y = to_categorical(y)

x.shape

y.shape

model = Sequential()
model.add(Conv2D(32, (5, 5), activation='relu', input_shape=(28, 28, 1)))
model.add(Dropout(0.2))
model.add(MaxPooling2D((2, 2)))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.2))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.summary()

history = model.fit(x, y, validation_split=0.2, epochs=30, batch_size=150)

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()

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model acc')
plt.ylabel('acc')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()

model.save_weights('val_accuracy_92.h5 ')

all_img = []
for i in range(0,10):
  img1 = cv2.imread(str(i)+'.jpg',0)
  (thresh, im_bw) = cv2.threshold(img1, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
  inv = cv2.bitwise_not(im_bw)
  all_img.append(cv2.resize(inv, (28,28)))

all_img = np.array(all_img)

for i in range(1,11):
  plt.subplot(2,5,i)
  plt.imshow(all_img[i-1])
  plt.title(np.argmax(model.predict(np.reshape(all_img[i-1],(-1,28,28,1)))))