main.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
#Libraries for implementing a CNN
from keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Dropout
from keras.models import Sequential
from keras.utils import to_categorical

# loading data
train = pd.read_csv('sign_mnist_train.csv')
test = pd.read_csv('sign_mnist_test.csv')

labels = train.pop('label')  #Pops the label column and stores in 'labels'
labels = to_categorical(labels)
train = train.values
train = np.array([np.reshape(i, (28,28)) for i in train])
train = train / 255

X_train, X_val, y_train, y_val = train_test_split(train, labels, test_size=0.3, random_state=41)
#Reshaping the training and validation sets
X_train = X_train.reshape(X_train.shape[0], 28,28,1)
X_val = X_val.reshape(X_val.shape[0], 28,28,1)

#Building Our CNN
model = Sequential()
model.add(Conv2D(8, (3,3), input_shape=(28,28,1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(16, (3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(25, activation='softmax'))
model.summary()

model.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
#Code for Training our Model
history = model.fit(X_train, y_train, validation_data = (X_val, y_val), epochs=50, batch_size=512)

plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title("Accuracy")
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.legend(['train','test'])
plt.show()

y_test = test.pop('label')
y_test = to_categorical(y_test)
y_test.shape
X_test = test.values
X_test = np.array([np.reshape(i, (28,28)) for i in X_test])
X_test = X_test / 255
X_test = X_test.reshape(X_test.shape[0], 28,28,1)
X_test.shape

#Recognizing images on the test dataset
predictions = model.predict(X_test)
test_accuracy = accuracy_score(np.argmax(y_test, axis=1), np.argmax(predictions, axis=1))
print("The test accuracy is: ", test_accuracy)