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Day 2 - MNIST Handwritten Digit Recognition
Hello guys,
This is day 2 of my #100DayOfMLCode challenge. Today, I implemented the MNIST handwritten digit recognition task in Python using the Keras deep learning library.
I studied the following topics:-
- How to load the MNIST dataset in Keras.
- How to develop and evaluate a baseline neural network model for the MNIST problem.
- How to implement and evaluate a simple Convolutional Neural Network for MNIST.
- How to implement a close to state-of-the-art deep learning model for MNIST.
Loading the MNIST dataset in Keras
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| from keras.datasets import mnist | |
| import matplotlib.pyplot as plt | |
| # load (downloaded if needed) the MNIST dataset | |
| (X_train, y_train), (X_test, y_test) = mnist.load_data() | |
| # plot 4 images as gray scale | |
| plt.subplot(221) | |
| plt.imshow(X_train[0], cmap=plt.get_cmap('gray')) | |
| plt.subplot(222) | |
| plt.imshow(X_train[1], cmap=plt.get_cmap('gray')) | |
| plt.subplot(223) | |
| plt.imshow(X_train[2], cmap=plt.get_cmap('gray')) | |
| plt.subplot(224) | |
| plt.imshow(X_train[3], cmap=plt.get_cmap('gray')) | |
| # show the plot | |
| plt.show() |
Output- (takes some time to show the output)
Baseline Model with Multi-Layer Perceptrons
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| #importing libraries | |
| import numpy | |
| from keras.datasets import mnist | |
| from keras.models import Sequential | |
| from keras.layers import Dense | |
| from keras.layers import Dropout | |
| from keras.utils import np_utils | |
| # fix random seed for reproducibility | |
| seed = 7 | |
| numpy.random.seed(seed) | |
| # load data | |
| (X_train, y_train), (X_test, y_test) = mnist.load_data() | |
| # flatten 28*28 images to a 784 vector for each image | |
| num_pixels = X_train.shape[1] * X_train.shape[2] | |
| X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32') | |
| X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32') | |
| # normalize inputs from 0-255 to 0-1 | |
| X_train = X_train / 255 | |
| X_test = X_test / 255 | |
| # one hot encode outputs | |
| y_train = np_utils.to_categorical(y_train) | |
| y_test = np_utils.to_categorical(y_test) | |
| num_classes = y_test.shape[1] | |
| # one hot encode outputs | |
| y_train = np_utils.to_categorical(y_train) | |
| y_test = np_utils.to_categorical(y_test) | |
| num_classes = y_test.shape[1] | |
| # define baseline model | |
| def baseline_model(): | |
| # create model | |
| model = Sequential() | |
| model.add(Dense(num_pixels, input_dim=num_pixels, kernel_initializer='normal', activation='relu')) | |
| model.add(Dense(num_classes, kernel_initializer='normal', activation='softmax')) | |
| # Compile model | |
| model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) | |
| return model | |
| # build the model | |
| model = baseline_model() | |
| # Fit the model | |
| model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2) | |
| # Final evaluation of the model | |
| scores = model.evaluate(X_test, y_test, verbose=0) | |
| print("Baseline Error: %.2f%%" % (100-scores[1]*100)) |
Output-
Simple Convolutional Neural Network for MNIST
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| import numpy | |
| from keras.datasets import mnist | |
| 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.utils import np_utils | |
| from keras import backend as K | |
| K.set_image_dim_ordering('th') | |
| # fix random seed for reproducibility | |
| seed = 7 | |
| numpy.random.seed(seed) | |
| # load data | |
| (X_train, y_train), (X_test, y_test) = mnist.load_data() | |
| # reshape to be [samples][pixels][width][height] | |
| X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32') | |
| X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32') | |
| # normalize inputs from 0-255 to 0-1 | |
| X_train = X_train / 255 | |
| X_test = X_test / 255 | |
| # one hot encode outputs | |
| y_train = np_utils.to_categorical(y_train) | |
| y_test = np_utils.to_categorical(y_test) | |
| num_classes = y_test.shape[1] | |
| def baseline_model(): | |
| # create model | |
| model = Sequential() | |
| model.add(Conv2D(32, (5, 5), input_shape=(1, 28, 28), activation='relu')) | |
| 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')) | |
| # Compile model | |
| model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) | |
| return model | |
| # build the model | |
| model = baseline_model() | |
| # Fit the model | |
| model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2) | |
| # Final evaluation of the model | |
| scores = model.evaluate(X_test, y_test, verbose=0) | |
| print("CNN Error: %.2f%%" % (100-scores[1]*100)) |
Output-
Thanks.
Happy Coding
Happy Coding
