hsleonis · December 8, 2021 00:51 · Dec 8, 2021
diff --git a/lenet.py b/lenet.py
@@ -0,0 +1,56 @@
+# -*- coding: utf-8 -*-
+"""
+LeNet - Deep Learning Neural Network
+"""
+
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.layers import Conv2D, AveragePooling2D, Dense, Flatten
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.datasets import mnist
+from tensorflow.keras.utils import to_categorical 
+import matplotlib.pyplot as plt
+
+# Load the MNIST dataset using the Keras library:
+(x_train , y_train), (x_test, y_test) = mnist.load_data()
+
+# Split dataset into training and testing, padding and normalizing images:
+x_train = np.pad(x_train,((0,0),(2,2),(2,2)))   # padding
+x_test  = np.pad(x_test,((0,0),(2,2),(2,2)))
+x_train = x_train/255.0                         # Normalizing the values
+x_test  = x_test/255.0
+y_train = to_categorical(y_train,10)            # converting labels into one-hot-encoded vectors
+y_test  = to_categorical(y_test,10)
+
+# Explore Dataset:
+plt.imshow(x_train[0], cmap='gray')
+plt.show()
+
+# Expand dimensions of dataset, because LeNet expects images of size 32x32x1 instead of 32x32:
+x_train = np.expand_dims(x_train, 3)
+x_test  = np.expand_dims(x_test, 3)
+
+# LeNet Model:
+lenet = Sequential(name="LeNet-5")
+lenet.add(Conv2D(6,(5,5),strides=(1,1), activation='tanh', input_shape=(32,32,1), name='C1')) # C1
+lenet.add(AveragePooling2D(name='S2')) # S2
+lenet.add(Conv2D(16,(5,5),strides=(1,1), activation='tanh', name='C3')) # C3
+lenet.add(AveragePooling2D(name='S4')) # S4
+lenet.add(Flatten())
+lenet.add(Dense(120,activation='tanh', name='FC5'))      # FC5
+lenet.add(Dense(84,activation='tanh', name='FC6'))       # FC6
+lenet.add(Dense(10,activation='softmax', name='Output')) # FC7
+
+# Compile model:
+lenet.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+# Model preview:
+lenet.summary()
+
+# Train model:
+lenet.fit(x_train, y_train, epochs=10, batch_size=32)
+
+# Evaluate model's performance by passing the test dataset:
+_, acc = lenet.evaluate(x_test, y_test)
+
+"""We aquire 98.58% accuracy."""
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