79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
import tensorflow as tf
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from tensorflow import keras
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import numpy as np
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import os
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# Version Information
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# tensorflow 2.2.0 , Cudnn7.6.5 and Cuda 10.1 , python 3.8
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from keras import backend as K
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K.clear_session()
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gpus = tf.config.experimental.list_physical_devices('GPU')
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if gpus:
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try:
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tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=6024)])
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except RuntimeError as e:
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print(e)
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os.exit(1)
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print(tf.config.experimental.list_physical_devices())
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print(tf.__version__)
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print(tf.test.is_built_with_cuda())
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(X_train, y_train), (X_test,y_test) = tf.keras.datasets.cifar10.load_data()
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(X_train, y_train), (X_test,y_test) = tf.keras.datasets.cifar10.load_data()
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print(X_train.shape,y_train.shape)
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classes = ["airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"]
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print(classes[y_train[3][0]])
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print("pre processing: scale images")
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X_train_scaled = X_train / 255
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X_test_scaled = X_test / 255
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y_train_categorical = keras.utils.to_categorical(
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y_train, num_classes=10, dtype='float32'
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)
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y_test_categorical = keras.utils.to_categorical(
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y_test, num_classes=10, dtype='float32'
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)
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print("model build")
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model = keras.Sequential([
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keras.layers.Flatten(input_shape=(32,32,3)),
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keras.layers.Dense(300, activation='relu'),
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keras.layers.Dense(100, activation='relu'),
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keras.layers.Dense(10, activation='sigmoid')
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])
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model.compile(optimizer='SGD',
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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model.fit(X_train_scaled, y_train_categorical, epochs=1)
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def get_model():
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model = keras.Sequential([
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keras.layers.Flatten(input_shape=(32,32,3)),
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keras.layers.Dense(3000, activation='relu'),
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keras.layers.Dense(1000, activation='relu'),
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keras.layers.Dense(10, activation='sigmoid')
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])
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model.compile(optimizer='SGD',
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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return model
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with tf.device('/GPU:0'):
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cpu_model = get_model()
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cpu_model.fit(X_train_scaled, y_train_categorical, epochs=10)
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print("done") |