5. Neural Architecture Search (Basic)

Open In Colab

In this tutorial we will learn the basics of neural architecture search (NAS). We will use artificial data generated from a polynomial function. Then, we will discover how to create a search space of neural architecture using a directed graph. Finally, we will see how to define the NAS settings and how to execute the search.

[1]:
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Warning

By design asyncio does not allow nested event loops. Jupyter is using Tornado which already starts an event loop. Therefore the following patch is required to run this tutorial.

[2]:
!pip install nest_asyncio

import nest_asyncio
nest_asyncio.apply()
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5.1. Loading the data

First, we will create the load_data function which loads and returns the training and validation data. The load_data function generates data from a function \(f\) where \(\mathbf{x} \in [a, b]^n\) such as \(f(\mathbf{x}) = -\sum_{i=0}^{n-1} {x_i ^2}\):

[3]:
import numpy as np

def load_data(verbose=0, dim=10, a=-50, b=50, prop=0.80, size=10000):
    rs = np.random.RandomState(2018)

    def polynome_2(x):
        return -sum([x_i ** 2 for x_i in x])

    d = b - a
    x = np.array([a + rs.random(dim) * d for _ in range(size)])
    y = np.array([[polynome_2(v)] for v in x])

    sep_index = int(prop * size)
    X_train = x[:sep_index]
    y_train = y[:sep_index]

    X_valid = x[sep_index:]
    y_valid = y[sep_index:]

    if verbose:
        print(f"X_train shape: {np.shape(X_train)}")
        print(f"y_train shape: {np.shape(y_train)}")
        print(f"X_valid shape: {np.shape(X_valid)}")
        print(f"y_valid shape: {np.shape(y_valid)}")
    return (X_train, y_train), (X_valid, y_valid)


_ = load_data(verbose=1)
X_train shape: (8000, 10)
y_train shape: (8000, 1)
X_valid shape: (2000, 10)
y_valid shape: (2000, 1)

5.2. Define a neural architecture search space

Let us define the neural architecture search space. To do this we use a KSearchSpace class. We define the ResNetMLPSpace search space which is a sub-class of KSearchSpace where we have to implement a build() method which return itself. The __init__ method is used to pass possible options of the search space such as the maximum number of layers self.num_layers.

The input nodes can be retrieved with self.input_nodes which is automatically built depending on the input_shape.

The search space is composed of ConstantNode and VariableNode. A ConstantNode defines a fixed operations whereas the VariableNode defines a list of possible operations (i.e., corresponds to a categorical decision variable). Operations can be defined directly from Keras Layers such as:

Activation = operation(tf.keras.layers.Activation)

All nodes of the search space without outer edges are automatically assumed to be output nodes.

[4]:
import collections

import tensorflow as tf

from deephyper.nas import KSearchSpace
from deephyper.nas.node import ConstantNode, VariableNode
from deephyper.nas.operation import operation, Zero, Connect, AddByProjecting, Identity


Activation = operation(tf.keras.layers.Activation)
Dense = operation(tf.keras.layers.Dense)
Dropout = operation(tf.keras.layers.Dropout)
Add = operation(tf.keras.layers.Add)
Flatten = operation(tf.keras.layers.Flatten)

ACTIVATIONS = [
    tf.keras.activations.elu,
    tf.keras.activations.gelu,
    tf.keras.activations.hard_sigmoid,
    tf.keras.activations.linear,
    tf.keras.activations.relu,
    tf.keras.activations.selu,
    tf.keras.activations.sigmoid,
    tf.keras.activations.softplus,
    tf.keras.activations.softsign,
    tf.keras.activations.swish,
    tf.keras.activations.tanh,
]


class ResNetMLPSpace(KSearchSpace):

    def __init__(self, input_shape, output_shape, seed=None, num_layers=3, mode="regression"):
        super().__init__(input_shape, output_shape, seed=seed)

        self.num_layers = num_layers
        assert mode in ["regression", "classification"]
        self.mode = mode

    def build(self):

        source = self.input_nodes[0]
        output_dim = self.output_shape[0]

        out_sub_graph = self.build_sub_graph(source, self.num_layers)

        if self.mode == "regression":
            output = ConstantNode(op=Dense(output_dim))
            self.connect(out_sub_graph, output)
        else:
            output = ConstantNode(
                op=Dense(output_dim, activation="softmax")
            )  # One-hot encoding
            self.connect(out_sub_graph, output)

        return self

    def build_sub_graph(self, input_, num_layers=3):
        source = prev_input = input_

        # look over skip connections within a range of the 3 previous nodes
        anchor_points = collections.deque([source], maxlen=3)

        for _ in range(self.num_layers):
            dense = VariableNode()
            self.add_dense_to_(dense)
            self.connect(prev_input, dense)
            x = dense

            dropout = VariableNode()
            self.add_dropout_to_(dropout)
            self.connect(x, dropout)
            x = dropout

            add = ConstantNode()
            add.set_op(AddByProjecting(self, [x], activation="relu"))

            for anchor in anchor_points:
                skipco = VariableNode()
                skipco.add_op(Zero())
                skipco.add_op(Connect(self, anchor))
                self.connect(skipco, add)

            prev_input = add

            # ! for next iter
            anchor_points.append(prev_input)

        return prev_input

    def add_dense_to_(self, node):
        node.add_op(Identity())  # we do not want to create a layer in this case
        for units in range(16, 16 * 16 + 1, 16):
            for activation in ACTIVATIONS:
                node.add_op(Dense(units=units, activation=activation))

    def add_dropout_to_(self, node):
        a, b = 1e-3, 0.4
        node.add_op(Identity())
        dropout_range = np.exp(np.linspace(np.log(a), np.log(b), 10))  #! NAS
        for rate in dropout_range:
            node.add_op(Dropout(rate))

A KSearchSpace as some useful methods such as:

  • space.sample(choice) which returns a random model from the search space if choice == None or generate a model corresponding to the choice if not.

  • space.choices() which returns the list of discrete dimensions corresponding to the search space.

Let us visualize a few randomly sampled neural architecture from this search space.

[6]:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from tensorflow.keras.utils import plot_model

shapes = dict(input_shape=(10,), output_shape=(1,))
space = ResNetMLPSpace(**shapes).build()

print("Choices: ", space.choices())

images = []
plt.figure(figsize=(15,15))
for i in range(4):

    plt.subplot(2,2,i+1)
    model = space.sample()
    plot_model(model, "random_model.png",
               show_shapes=False, show_layer_names=False)
    image = mpimg.imread("random_model.png")
    plt.imshow(image)
    plt.axis('off')

plt.show()
Choices:  [(0, 176), (0, 10), (0, 1), (0, 176), (0, 10), (0, 1), (0, 1), (0, 176), (0, 10), (0, 1), (0, 1), (0, 1)]
../../../_images/tutorials_tutorials_colab_NAS_basic_9_1.png

5.3. Create a problem instance

Let us define the neural architecture search problem.

[7]:
from deephyper.problem import NaProblem
from deephyper.nas.preprocessing import minmaxstdscaler

# Create a Neural Architecture problem
problem = NaProblem()

# Link the load-data function
problem.load_data(load_data)

# The function passed to preprocessing has to return
# a scikit-learn like preprocessor.
problem.preprocessing(minmaxstdscaler)

# Link the defined search space
problem.search_space(ResNetMLPSpace)

# Fixed hyperparameters for all trained models
problem.hyperparameters(
    batch_size=32,
    learning_rate=0.01,
    optimizer="adam",
    num_epochs=20,
    callbacks=dict(
        EarlyStopping=dict(
            monitor="val_r2", mode="max", verbose=0, patience=5
        )
    ),
)

# Define the optimized loss (it can also be a function)
problem.loss("mse")

# Define metrics to compute for each training and validation epoch
problem.metrics(["r2"])

# Define the maximised objective
problem.objective("val_r2__last")

problem
[7]:
Problem is:
    - search space   : __main__.ResNetMLPSpace
    - data loading   : __main__.load_data
    - preprocessing  : deephyper.nas.preprocessing._base.minmaxstdscaler
    - hyperparameters:
        * verbose: 0
        * batch_size: 32
        * learning_rate: 0.01
        * optimizer: adam
        * num_epochs: 20
        * callbacks: {'EarlyStopping': {'monitor': 'val_r2', 'mode': 'max', 'verbose': 0, 'patience': 5}}
    - loss           : mse
    - metrics        :
        * r2
    - objective      : val_r2__last

Find more about NaProblem settings on the Problem documentation.

Tip

Adding an EarlyStopping(...) callback is a good idea to stop the training of your model as soon as it stops to improve.

...
EarlyStopping=dict(monitor="val_r2", mode="max", verbose=0, patience=5)
...

5.5. Testing the best configuration

We can visualize the architecture of the best configuration:

[17]:
best_config = results.iloc[results.objective.argmax()][:-2].to_dict()
arch_seq = json.loads(best_config["arch_seq"])
model = space.sample(arch_seq)
plot_model(model, show_shapes=False, show_layer_names=False)
[17]:
../../../_images/tutorials_tutorials_colab_NAS_basic_27_0.png