How to build a sequential Keras model?

To build a sequential Keras model, you can create an instance of the Sequential class and add layers to it using the add() method. Here is an example:

from tensorflow import keras

model = keras.Sequential()
model.add(keras.layers.Dense(64, activation='relu', input_shape=(100,))
model.add(keras.layers.Dense(10, activation='softmax'))

This creates a model with a 64-node dense layer using ReLU activation, followed by a 10-node dense layer using softmax activation. The input shape for this model is a 1D array of length 100.

You can continue adding layers to the model by calling add() again. Once you have added all the layers, you need to compile() the model with an optimizer and a loss function before you can call fit() to train it.

How to compile a Keras model?

To compile a Keras model, you need to specify a loss function, an optimizer, and one or more evaluation metrics. You can do this using the compile() method of the model. Here is an example:

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

In this example, we’re using binary cross-entropy as the loss function, the Adam optimizer, and accuracy as the evaluation metric. You can customize these as needed for your specific problem.

Once you have compiled the model, you can train it using the fit() method, passing in your training data and labels.

How to train a Keras model?

To train a Keras model, you can use the fit() method, passing in your training data and labels, as well as other parameters such as the number of epochs and batch size. Here is an example:

model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_val, y_val))

In this example, x_train and y_train are the training data and labels, respectively. epochs specifies the number of training epochs, batch_size is the number of training examples per batch, and validation_data is optional, and can be used for validation during training.

You can monitor the progress of training using the fit() method’s return value, which is a History object that contains information on the training and validation loss and metrics.

Once you have trained the model, you can evaluate it using the evaluate() method, passing in your test data and labels, to get a measure of its performance on unseen data.

How to evaluate a Keras model?

To evaluate a Keras model, you can use the evaluate() method, passing in your test data and labels. Here is an example:

loss, accuracy = model.evaluate(x_test, y_test)

In this example, x_test and y_test are the test data and labels, respectively. The evaluate() method returns the overall loss and the specified metrics, such as accuracy.

You can also use the predict() method to get predictions for new data, and then compare those predictions against the ground truth labels to get a measure of the model’s performance.

It’s important to note that when evaluating or predicting using a Keras model you should ensure that the data is preprocessed in the same way as during training, and to use the same evaluation metrics as specified during the compilation step.

How to save and load a Keras model?

To save a Keras model, you can use the save() method, which saves the model architecture, weights, and optimizer state. Here is an example:

model.save('my_model.h5')

In this example, my_model.h5 is the filename to which the model will be saved.

To load a saved Keras model, you can use the load_model() function from the tensorflow.keras.models module. Here is an example:

from tensorflow.keras.models import load_model

loaded_model = load_model('my_model.h5')

In this example, my_model.h5 is the name of the saved model file. The load_model() function returns a Keras model object that you can use to make predictions or further train the model.

Note that when you load a saved model, you need to use the same version of Keras or TensorFlow as used when the model was saved. Additionally, you should ensure that the data you use for predictions or further training is preprocessed in the same way as it was during training.