How does model.fit work? what is computed under the hood?

model.fit is a function in Keras that trains the model for a fixed number of epochs (iterations on a dataset). It works by taking the training data and labels as input, and using these to adjust the model’s weights through a process called backpropagation and gradient descent.

Here’s what happens under the hood:

1. The model makes predictions on the training data.
2. The loss function compares these predictions to the true labels and calculates a ‘loss’ value. This value represents how well the model is doing. The lower the loss, the better the model’s predictions.
3. The optimizer then adjusts the model’s weights in order to minimize this loss value.

This process is repeated for a fixed number of iterations, or ‘epochs’. An epoch is one complete pass through the entire training dataset.

Let’s look at three different scenarios:

1. Binary Classification: Let’s say you have a model that’s trying to predict whether a given email is spam or not. In this case, you might use the BinaryCrossentropy loss function. Your labels would be 0 (not spam) or 1 (spam), and model.fit would adjust the model’s weights to get as close as possible to these true labels.
2. Multi-Class Classification: Now let’s say you have a model that’s trying to recognize different types of animals in images. In this case, you might use the CategoricalCrossentropy or SparseCategoricalCrossentropy loss function. Your labels would be one-hot vectors or integers representing the different animal types, and model.fit would adjust the model’s weights to get as close as possible to these true labels.
3. Regression: Finally, let’s say you have a model that’s trying to predict house prices based on various features. In this case, you might use the MeanSquaredError loss function. Your labels would be the actual house prices, and model.fit would adjust the model’s weights to get as close as possible to these true labels.

As for tokens, in the context of natural language processing, tokens are the basic units of processing. For example, a sentence might be split into tokens representing individual words or subwords. These tokens are then converted into numerical vectors (using techniques like one-hot encoding or word embeddings) that can be fed into the model. The model’s weights are adjusted based on these vectors during the training process.

In model.fit, the number of epochs determines how many times this process of making predictions, calculating loss, and adjusting weights is repeated on the entire dataset. More epochs usually mean the model will learn better, but it also increases the risk of overfitting (where the model learns the training data too well and performs poorly on unseen data).

Let’s take a look at quick examples:

1. Binary Classification: Predicting whether an email is spam or not.

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Create a simple model
model = Sequential([
    Dense(10, activation='relu', input_shape=(100,)),
    Dense(1, activation='sigmoid'),
])

# Compile the model with Binary Crossentropy loss
model.compile(optimizer='adam', loss='binary_crossentropy')

# Let's say we have some input data and labels
data = tf.random.normal((1000, 100)) # 1000 examples, each with 100 features
labels = tf.random.uniform((1000, 1), minval=0, maxval=2, dtype=tf.int32) # 1000 labels, either 0 or 1

# Train the model
model.fit(data, labels, epochs=10)

In this example, we’re creating a model with one hidden layer of 10 neurons and an output layer with 1 neuron (since we have two classes, spam or not spam). The ‘sigmoid’ activation function in the output layer squashes the output between 0 and 1, giving us a probability of the email being spam.

2. Multi-Class Classification: Recognizing different types of animals in images.

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.losses import SparseCategoricalCrossentropy

# Create a simple model
model = Sequential([
    Flatten(input_shape=(32, 32, 3)), # assuming 32x32 color images
    Dense(64, activation='relu'),
    Dense(4, activation='softmax'), # assuming 4 types of animals
])

# Compile the model with Sparse Categorical Crossentropy loss
model.compile(optimizer='adam', loss=SparseCategoricalCrossentropy())

# Let's say we have some input data and labels
data = tf.random.normal((1000, 32, 32, 3)) # 1000 examples, each a 32x32 color image
labels = tf.random.uniform((1000,), minval=0, maxval=4, dtype=tf.int32) # 1000 labels, each one of 4 classes

# Train the model
model.fit(data, labels, epochs=10)

In this example, we’re creating a model with one hidden layer of 64 neurons and an output layer with 4 neurons (since we have four types of animals). The ‘softmax’ activation function in the output layer gives us a probability distribution over the four classes.

3. Regression: Predicting house prices.

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Create a simple model
model = Sequential([
    Dense(10, activation='relu', input_shape=(100,)),
    Dense(1),
])

# Compile the model with Mean Squared Error loss
model.compile(optimizer='adam', loss='mean_squared_error')

# Let's say we have some input data and labels
data = tf.random.normal((1000, 100)) # 1000 examples, each with 100 features
labels = tf.random.normal((1000, 1)) # 1000 labels, each a real number representing house price

# Train the model
model.fit(data, labels, epochs=10)

In this example, we’re creating a model with one hidden layer of 10 neurons and an output layer with 1 neuron (since we’re predicting a single continuous value, the house price). The output layer has no activation function, allowing it to output any real number.