François Chollet – The Evolution of Keras: Introducing Version 3

François Chollet is a well-known data scientist and the creator of Keras, a popular deep learning library written in Python. In this tutorial, we will explore how to create a Keras 3 model using François Chollet’s techniques.

Step 1: Install Keras 3
Before we can create a Keras 3 model, we first need to install the Keras library. You can do this using the following command:

pip install keras

Step 2: Import the necessary libraries
Once Keras is installed, we can start creating our model. The first step is to import the necessary libraries. In François Chollet’s Keras 3, we typically import Keras along with some other commonly used libraries like numpy and matplotlib. Here’s an example of how to do this:

import keras
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
import matplotlib.pyplot as plt

Step 3: Define the data
Next, we need to define the data that we will use to train our model. For this tutorial, let’s create a simple dataset of X and y values:

X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
y = np.array([2, 4, 6, 8, 10, 12, 14, 16, 18, 20])

Step 4: Create the model
Now that we have our data, we can create our Keras 3 model. In François Chollet’s Keras 3, we typically use a Sequential model with a few dense layers. Here’s an example of how to create a simple linear regression model:

model = Sequential()
model.add(Dense(1, input_dim=1, activation='linear'))

Step 5: Compile the model
After creating the model, we need to compile it. This step involves specifying the loss function, optimizer, and metrics that will be used to train the model. For this tutorial, let’s use the mean squared error loss function and stochastic gradient descent optimizer:

model.compile(loss='mean_squared_error', optimizer='sgd')

Step 6: Train the model
With the model compiled, we can now train it on our data. To do this, we use the fit method and specify the number of epochs (iterations) that the model will be trained for:

model.fit(X, y, epochs=100)

Step 7: Make predictions
Once the model has been trained, we can use it to make predictions on new data. Let’s generate some new X values and use the model to predict corresponding y values:

X_new = np.array([11, 12, 13, 14, 15])
predictions = model.predict(X_new)
print(predictions)

Step 8: Visualize the results
Finally, we can visualize the results of our model by plotting the original data points along with the predicted values. Here’s an example of how to do this using matplotlib:

plt.scatter(X, y, color='red')
plt.plot(X_new, predictions, color='blue')
plt.show()

And that’s it! You have now created a Keras 3 model using François Chollet’s techniques. Feel free to experiment with different architectures, loss functions, and optimizers to see how they affect the performance of your model.