requirements.yml：
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| name: apple_tensorflow
channels:
- conda-forge
- nodefaults
dependencies:
- absl-py
- astunparse
- gast
- google-pasta
- grpcio
- h5py
- ipython
- keras-preprocessing
- numpy
- opt_einsum
- pip=20.2.4
- protobuf
- python-flatbuffers
- python=3.8
- scipy
- tensorboard
- tensorflow-estimator
- termcolor
- typeguard
- typing_extensions
- wheel
- wrapt
|
conda env create --file=PATH_TO_ENVIRONMENT.YML --name=tf
conda activate tf
pip install --upgrade --force --no-dependencies https://github.com/apple/tensorflow_macos/releases/download/v0.1alpha3/tensorflow_addons_macos-0.1a3-cp38-cp38-macosx_11_0_arm64.whl https://github.com/apple/tensorflow_macos/releases/download/v0.1alpha3/tensorflow_macos-0.1a3-cp38-cp38-macosx_11_0_arm64.whl
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| import tensorflow as tf
import time
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
start = time.time()
model.fit(x_train, y_train, epochs=5)
end = time.time()
model.evaluate(x_test, y_test)
print(end - start)
|
