deep_learning.cnn()

Complexity

Conditions

1

Size

Total Lines	38
Code Lines	30

Duplication

Lines	0
Ratio	0 %

Importance

Changes

0

from keras.models import Sequential
def cnn(para):
    nn = Sequential()
    nn.add(
        Conv2D(
            para["filter.0"],
            (3, 3),
            padding="same",
            input_shape=X_train.shape[1:],
        )
    )
    nn.add(Activation("relu"))
    nn.add(Conv2D(para["filter.0"], (3, 3)))
    nn.add(Activation("relu"))
    nn.add(MaxPooling2D(pool_size=(2, 2)))
    nn.add(Dropout(0.25))

    nn.add(Conv2D(para["filter.0"], (3, 3), padding="same"))
    nn.add(Activation("relu"))
    nn.add(Conv2D(para["filter.0"], (3, 3)))
    nn.add(Activation("relu"))
    nn.add(MaxPooling2D(pool_size=(2, 2)))
    nn.add(Dropout(0.25))

    nn.add(Flatten())
    nn.add(Dense(para["dense.0"]))
    nn.add(Activation("relu"))
    nn.add(Dropout(0.5))
    nn.add(Dense(10))
    nn.add(Activation("softmax"))

    nn.compile(
        optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]
    )
    nn.fit(X_train, y_train, epochs=25, batch_size=128)

    _, score = nn.evaluate(x=X_test, y=y_test)

    return score