python-tensorflowHow can I use a Python TensorFlow session to build a machine learning model?

Using Python TensorFlow, you can build a machine learning model by first defining the model's architecture. This can be done using the tf.keras.Sequential API. For example:

model = tf.keras.Sequential([
  tf.keras.layers.Dense(128, activation='relu', input_shape=(784,)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(10, activation='softmax')
])

The above code creates a model with three layers:

1. A Dense layer with 128 neurons and the relu activation function. The input_shape parameter specifies the shape of the input data (784 in this case).

2. A second Dense layer with 128 neurons and the relu activation function.

3. A third Dense layer with 10 neurons and the softmax activation function.

Once the model's architecture is defined, you can compile it by specifying the optimizer and loss function. For example:

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

The above code compiles the model with the sgd optimizer and categorical_crossentropy loss function. It also specifies the accuracy metric to be used for evaluation.

Finally, you can train the model using the model.fit API. For example:

model.fit(x_train, y_train, epochs=5)

The above code trains the model on the training data (x_train and y_train) for 5 epochs.

Helpful links

• TensorFlow Keras Sequential API
• TensorFlow Compile API
• TensorFlow Fit API

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