python-tensorflowHow do I use Python and TensorFlow to fit a model?

Using Python and TensorFlow to fit a model is a powerful way to create a machine learning model. To do this, the following steps should be taken:

1. Define the model's architecture: This involves setting up the layers, nodes, and connections between them.

2. Compile the model: This involves specifying the optimizer, the loss function, and the metrics for evaluating the model's performance.

3. Train the model: This involves feeding the training data into the model and adjusting the weights and biases of the model's parameters to minimize the loss.

4. Evaluate the model: This involves running the model on the test data to measure its performance.

Example code

# Define the model
model = keras.Sequential([
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
model.fit(train_images, train_labels, epochs=5)

# Evaluate the model
test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

Output example

Epoch 1/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.4950 - accuracy: 0.8249
Epoch 2/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.3716 - accuracy: 0.8657
Epoch 3/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.3314 - accuracy: 0.8786
Epoch 4/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.3045 - accuracy: 0.8886
Epoch 5/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.2815 - accuracy: 0.8949
313/313 - 0s - loss: 0.3519 - accuracy: 0.8719

Helpful links

• TensorFlow Tutorials
• Keras Documentation

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