TensorFlow steps, savers, and utilities for Neuraxle.
Neuraxle is a Machine Learning (ML) library for building neat pipelines, providing the right abstractions to both ease research, development, and deployment of your ML applications.
Create a tensorflow 1 model step by giving it a graph, an optimizer, and a loss function.
def create_graph(step: TensorflowV1ModelStep):
tf.placeholder('float', name='data_inputs')
tf.placeholder('float', name='expected_outputs')
tf.Variable(np.random.rand(), name='weight')
tf.Variable(np.random.rand(), name='bias')
return tf.add(tf.multiply(step['data_inputs'], step['weight']), step['bias'])
"""
# Note: you can also return a tuple containing two elements : tensor for training (fit), tensor for inference (transform)
def create_graph(step: TensorflowV1ModelStep)
# ...
decoder_outputs_training = create_training_decoder(step, encoder_state, decoder_cell)
decoder_outputs_inference = create_inference_decoder(step, encoder_state, decoder_cell)
return decoder_outputs_training, decoder_outputs_inference
"""
def create_loss(step: TensorflowV1ModelStep):
return tf.reduce_sum(tf.pow(step['output'] - step['expected_outputs'], 2)) / (2 * N_SAMPLES)
def create_optimizer(step: TensorflowV1ModelStep):
return tf.train.GradientDescentOptimizer(step.hyperparams['learning_rate'])
model_step = TensorflowV1ModelStep(
create_grah=create_graph,
create_loss=create_loss,
create_optimizer=create_optimizer,
has_expected_outputs=True
).set_hyperparams(HyperparameterSamples({
'learning_rate': 0.01
})).set_hyperparams_space(HyperparameterSpace({
'learning_rate': LogUniform(0.0001, 0.01)
}))Create a tensorflow 2 model step by giving it a model, an optimizer, and a loss function.
def create_model(step: Tensorflow2ModelStep):
return LinearModel()
def create_optimizer(step: Tensorflow2ModelStep):
return tf.keras.optimizers.Adam(0.1)
def create_loss(step: Tensorflow2ModelStep, expected_outputs, predicted_outputs):
return tf.reduce_mean(tf.abs(predicted_outputs - expected_outputs))
model_step = Tensorflow2ModelStep(
create_model=create_model,
create_optimizer=create_optimizer,
create_loss=create_loss,
tf_model_checkpoint_folder=os.path.join(tmpdir, 'tf_checkpoints')
)