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LeNet5 handwritten digit recognition
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| from keras.datasets import mnist | |
| from keras.utils import to_categorical | |
| from keras.layers import * | |
| from keras.models import * | |
| import numpy as np | |
| ## Load and preprocess the MNIST dataset | |
| (train_images , train_labels) , (validation_images , validation_labels) = mnist.load_data() | |
| ## reshaping the images and normalizing the pixel values to be between 0 and 1. | |
| train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255 | |
| validation_images = validation_images.reshape((10000, 28, 28, 1)).astype('float32') / 255 | |
| train_labels = to_categorical(train_labels) | |
| validation_labels = to_categorical(validation_labels) | |
| # Define The LeNet-5 Architechture | |
| model = Sequential() | |
| #Layer 1 | |
| #Conv Layer 1 | |
| model.add(Conv2D(filters = 6, | |
| kernel_size = 5, | |
| strides = 1, | |
| activation = 'relu', | |
| input_shape = (28,28,1))) | |
| #Pooling layer 1 | |
| model.add(MaxPooling2D(pool_size = 2, strides = 2)) | |
| #Layer 2 | |
| #Conv Layer 2 | |
| model.add(Conv2D(filters = 16, | |
| kernel_size = 5, | |
| strides = 1, | |
| activation = 'relu', | |
| input_shape = (14,14,6))) | |
| #Pooling Layer 2 | |
| model.add(MaxPooling2D(pool_size = 2, strides = 2)) | |
| #Flatten | |
| model.add(Flatten()) | |
| #Layer 3 | |
| #Fully connected layer 1 | |
| model.add(Dense(units = 120, activation = 'relu')) | |
| #Layer 4 | |
| #Fully connected layer 2 | |
| model.add(Dense(units = 84, activation = 'relu')) | |
| model.add(Dropout(0.2)) | |
| #Layer 5 | |
| #Output Layer | |
| model.add(Dense(units = 10, activation = 'softmax')) | |
| # Compile the model | |
| model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy']) | |
| print(model.summary()) | |
| # Train the model | |
| model.fit(train_images,train_labels , batch_size=128, epochs=20, verbose=1, validation_data=(validation_images, validation_labels)) | |
| # Prediction for validation_images | |
| y_pred = model.predict(validation_images) | |
| #Converting one hot vectors to labels | |
| labels = np.argmax(y_pred, axis = 1) | |
| print(labels) | |
| ## Evaluate the model | |
| Loss , Accuracy = model.evaluate(validation_images, validation_labels) | |
| print(f"Loss :{Loss} , Accuracy : {Accuracy * 100:.3f}%") |
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