Question

我正在使用Tensorflow和Keras在Python中构建一个简单的神经网络。我需要使用PyCuda实现此代码以在GPU上工作。我计划并行学习所有纪元，但是由于Keras非常简约，因此所有纪元培训（至少根据我的理解）都是一行完成的：

model.fit（train_images，train_labels，epochs = 100）

如何从此函数中“提取”某些东西，并将其提供给PyCuda内核函数？到目前为止，这是我的代码：

#TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

#Helper libraries
import numpy as np
import matplotlib.pyplot as plt
import cv2

print(tf.__version__)

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

train_images.shape
len(train_labels)
train_labels
test_images.shape
len(test_labels)

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()

train_images = train_images / 255.0
test_images = test_images / 255.0

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])

plt.show()

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])

model.compile(optimizer=tf.train.AdamOptimizer(),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=100)

Keras函数model.fit（）的源代码是什么？

0 个答案: