python-tensorflowHow do I use Python TensorFlow.Keras.Models to create a machine learning model?

Python TensorFlow.Keras.Models can be used to create a machine learning model. To do this, the following steps can be taken:

1. First, import the relevant libraries:

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

2. Then, create the model structure by defining the layers:

model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))

3. Compile the model, specifying the optimizer, loss function and metrics:

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

4. Fit the model to the training data for a specified number of epochs:

model.fit(train_data, train_targets, epochs=5)

5. Evaluate the model on the test data:

test_loss, test_acc = model.evaluate(test_data, test_targets)

6. Make predictions using the model:

predictions = model.predict(test_data)

7. Finally, save the model for future use:

model.save('my_model.h5')

Helpful links

• TensorFlow - Get Started
• Keras - Getting Started
• Keras - Model

Edit this code on GitHub