- 官方测试代码：
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)
- 测试平台成绩：
windows 11:
- cpu: r7-3700x 8 core
- gpu: GTX-1060
- ram: 32GB DDR4-3200Mhz
-
tensorflow: v2.3.0
测试成绩
1634743513(1).png
macos 12:
- cpu: M1 8 core
- gpu: M1 7 core gpu
- ram: 8GB
-
tensorflow: v2.6.0
测试成绩：
9862d6ec49d310d64446961760b8c07.jpg
树莓派3B+
- cpu: 4 core 1.4Ghz Cortex-A53
- Ram: 1gb ddr2
-
tensorflow: v1.14
c20526df870bb9acbae223266a7b549.jpg
纯粹娱乐。。。
M1芯片的强势，1060功耗的强大，树莓派的节能。
