Add a base numerical test for keras core. (#171)
This commit is contained in:
parent
93715d04cc
commit
d07404036c
88
integration_tests/numerical_test.py
Normal file
88
integration_tests/numerical_test.py
Normal file
@ -0,0 +1,88 @@
|
||||
import numpy as np
|
||||
from tensorflow import keras
|
||||
|
||||
import keras_core
|
||||
|
||||
NUM_CLASSES = 10
|
||||
|
||||
|
||||
def build_mnist_data(num_classes):
|
||||
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
|
||||
|
||||
# Scale images to the [0, 1] range
|
||||
x_train = x_train.astype("float32") / 255
|
||||
x_test = x_test.astype("float32") / 255
|
||||
# Make sure images have shape (28, 28, 1)
|
||||
x_train = np.expand_dims(x_train, -1)
|
||||
x_test = np.expand_dims(x_test, -1)
|
||||
|
||||
# convert class vectors to binary class matrices
|
||||
y_train = keras.utils.to_categorical(y_train, num_classes)
|
||||
y_test = keras.utils.to_categorical(y_test, num_classes)
|
||||
|
||||
print("x_train shape:", x_train.shape)
|
||||
print(x_train.shape[0], "train samples")
|
||||
print(x_test.shape[0], "test samples")
|
||||
|
||||
return x_train, y_train, x_test, y_test
|
||||
|
||||
|
||||
def build_keras_model(keras_module, num_classes):
|
||||
|
||||
input_shape = (28, 28, 1)
|
||||
|
||||
model = keras_module.Sequential(
|
||||
[
|
||||
keras_module.Input(shape=input_shape),
|
||||
keras_module.layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
|
||||
keras_module.layers.MaxPooling2D(pool_size=(2, 2)),
|
||||
keras_module.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
|
||||
keras_module.layers.MaxPooling2D(pool_size=(2, 2)),
|
||||
keras_module.layers.Flatten(),
|
||||
keras_module.layers.Dropout(0.5),
|
||||
keras_module.layers.Dense(num_classes, activation="softmax"),
|
||||
]
|
||||
)
|
||||
|
||||
model.summary()
|
||||
return model
|
||||
|
||||
|
||||
def train_model(model, x, y):
|
||||
batch_size = 128
|
||||
epochs = 1
|
||||
|
||||
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
|
||||
|
||||
return model.fit(x, y, batch_size=batch_size, epochs=epochs, validation_split=0.1)
|
||||
|
||||
|
||||
def eval_model(model, x, y):
|
||||
score = model.evaluate(x, y, verbose=0)
|
||||
print("Test loss:", score[0])
|
||||
print("Test accuracy:", score[1])
|
||||
return score
|
||||
|
||||
|
||||
def numerical_test():
|
||||
x_train, y_train, x_test, y_test = build_mnist_data(NUM_CLASSES)
|
||||
keras_model = build_keras_model(keras, NUM_CLASSES)
|
||||
keras_core_model = build_keras_model(keras_core, NUM_CLASSES)
|
||||
|
||||
# Make sure both model have same weights before training
|
||||
keras_core_model.set_weights(keras_model.weights)
|
||||
|
||||
for kw, kcw in zip(keras_model.weights, keras_core_model.weights):
|
||||
np.testing.assert_allclose(kw, kcw)
|
||||
|
||||
keras_history = train_model(keras_model, x_train, y_train)
|
||||
keras_core_history = train_model(keras_core_model, x_train, y_train)
|
||||
|
||||
for h, ch in zip(keras_history.history.items(), keras_core_history.history.items()):
|
||||
# They are not exactly equal at the moment.
|
||||
print(h)
|
||||
print(ch)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
numerical_test()
|
Loading…
Reference in New Issue
Block a user