Quickstart

Training a model

Below, we define our own Keras Sequential and train it on a toy classification dataset using SciKeras KerasClassifier:

import numpy as np
from sklearn.datasets import make_classification
from tensorflow import keras

from scikeras.wrappers import KerasClassifier


X, y = make_classification(1000, 20, n_informative=10, random_state=0)
X = X.astype(np.float32)
y = y.astype(np.int64)

def get_model(hidden_layer_dim, meta):
    # note that meta is a special argument that will be
    # handed a dict containing input metadata
    n_features_in_ = meta["n_features_in_"]
    X_shape_ = meta["X_shape_"]
    n_classes_ = meta["n_classes_"]

    model = keras.models.Sequential()
    model.add(keras.layers.Dense(n_features_in_, input_shape=X_shape_[1:]))
    model.add(keras.layers.Activation("relu"))
    model.add(keras.layers.Dense(hidden_layer_dim))
    model.add(keras.layers.Activation("relu"))
    model.add(keras.layers.Dense(n_classes_))
    model.add(keras.layers.Activation("softmax"))
    return model

clf = KerasClassifier(
    get_model,
    loss="sparse_categorical_crossentropy",
    hidden_layer_dim=100,
)

clf.fit(X, y)
y_proba = clf.predict_proba(X)

In an sklearn Pipeline

Since KerasClassifier provides an sklearn-compatible interface, it is possible to put it into an sklearn Pipeline:

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler


pipe = Pipeline([
    ('scale', StandardScaler()),
    ('clf', clf),
])

pipe.fit(X, y)
y_proba = pipe.predict_proba(X)

Grid search

Another advantage of SciKeras is that you can perform an sklearn GridSearchCV or RandomizedSearchCV:

from sklearn.model_selection import GridSearchCV


params = {
    "hidden_layer_dim": [50, 100, 200],
    "loss": ["sparse_categorical_crossentropy"],
    "optimizer": ["adam", "sgd"],
    "optimizer__learning_rate": [0.0001, 0.001, 0.1],
}
gs = GridSearchCV(clf, params, refit=False, cv=3, scoring='accuracy')

gs.fit(X, y)
print(gs.best_score_, gs.best_params_)

What’s next?

Please visit the Tutorials page to explore additional examples on using SciKeras!