Visualizing Neural Network Predictions on MNIST Dataset Using Keras in Google Colab

We provide Handwritten images to recognize the Number or Letter written in the image.

This notebook will train a simple neural network on the MNIST dataset and include data visualization at the end.

Step 1: Set Up Google Colab

1. Open Google Colab: Google Colab
2. Create a new notebook.

Step 2: Copy and Paste the Notebook Code

Copy and paste the following code into your Google Colab notebook cells.

# Install necessary libraries
!pip install tensorflow matplotlib

# Step 3: Import Libraries
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.utils import to_categorical

# Step 4: Load and Prepare the Data
# Load the MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Normalize the input data
x_train = x_train / 255.0
x_test = x_test / 255.0

# One-hot encode the labels
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# Step 5: Build the Model
model = Sequential([
    Flatten(input_shape=(28, 28)),
    Dense(128, activation='relu'),
    Dense(64, activation='relu'),
    Dense(10, activation='softmax')
])

# Step 6: Compile the Model
model.compile(optimizer='adam', 
              loss='categorical_crossentropy', 
              metrics=['accuracy'])

# Step 7: Train the Model
history = model.fit(x_train, y_train, epochs=10, validation_split=0.2)

# Step 8: Evaluate the Model
test_loss, test_acc = model.evaluate(x_test, y_test)
print(f'Test accuracy: {test_acc}')

# Step 9: Data Visualization
# Plot the training history
plt.figure(figsize=(12, 4))

plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Accuracy')
plt.plot(history.history['val_accuracy'], label = 'Val Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.title('Model Accuracy')

plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Loss')
plt.plot(history.history['val_loss'], label = 'Val Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend(loc='upper right')
plt.title('Model Loss')

plt.show()

# Visualize some predictions
predictions = model.predict(x_test)

plt.figure(figsize=(10, 10))
for i in range(25):
    plt.subplot(5, 5, i + 1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(x_test[i], cmap=plt.cm.binary)
    plt.xlabel(f"True: {np.argmax(y_test[i])}, Pred: {np.argmax(predictions[i])}")
plt.show()        

Explanation of the Notebook

1. Install Libraries: Install TensorFlow and Matplotlib if not already installed.
2. Import Libraries: Import the necessary libraries for data handling, model creation, and visualization.
3. Load and Prepare the Data: Load the MNIST dataset, normalize the images, and one-hot encode the labels.
4. Build the Model: Create a simple neural network model using Keras.
5. Compile the Model: Compile the model with an optimizer, loss function, and metrics.
6. Train the Model: Train the model on the training data and validate it on a validation split.
7. Evaluate the Model: Evaluate the model on the test data and print the test accuracy.
8. Data Visualization: Plot the training history and visualize some predictions.

Running the Notebook

1. After copying the code into your Colab notebook, run each cell sequentially.
2. The notebook will train a neural network on the MNIST dataset and display accuracy and loss plots.
3. It will also visualize some predictions, showing the true labels and the model's predictions for some test images.

OUTPUT of this Notebook:

#keras #data #pytorch

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