!pwd # 用 pwd 命令顯示工作路徑

# /content

!ls # 查看的是 content 文件夾下有哪些文件

# sample_data

!ls 'drive/My Drive'

# TensorFlow （這就是我們之前創(chuàng)建的那個(gè)文件夾）

# 更改工作目錄

import os

os.chdir('/content/drive/My Drive/TensorFlow')

os.getcwd

# '/content/drive/My Drive/TensorFlow'

from google.colab import files

files.download('./example.txt') # 下載文件

# @title 字符串

text = 'value' #@param {type:'string'}

dropdown = '1st option' #@param ['1st option', '2nd option', '3rd option']

text_and_dropdown = 'value' #@param ['選項(xiàng)1', '選項(xiàng)2', '選項(xiàng)3'] {allow-input: true}

print(text)

print(dropdown)

print(text_and_dropdown)

import tensorflow as tf import timeit

config = tf.ConfigProto

config.gpu_options.allow_growth = True

with tf.device('/cpu:0'):

random_image_cpu = tf.random_normal((100,100,100,3))

net_cpu = tf.layers.conv2d(random_image_cpu, 32,7)

net_cpu = tf.reduce_sum(net_cpu)

with tf.device('/device:GPU:0'):

random_image_gpu = tf.random_normal((100,100,100,3))

net_gpu = tf.layers.conv2d(random_image_gpu, 32,7)

net_gpu = tf.reduce_sum(net_gpu)

sess = tf.Session(config=config) # 確保TF可以檢測(cè)到GPU

try:

sess.run(tf.global_variables_initializer) except tf.errors.InvalidArgumentError: print( 'nn此錯(cuò)誤很可能表示此筆記本未配置為使用GPU。'

'通過命令面板（CMD/CTRL-SHIFT-P）或編輯菜單在筆記本設(shè)置中更改此設(shè)置.nn') raise

def cpu:

sess.run(net_cpu) def gpu:

sess.run(net_gpu) # 運(yùn)行一次進(jìn)行測(cè)試

cpu

gpu # 多次運(yùn)行op

print('將100*100*100*3通過濾波器卷積到32*7*7*3(批處理x高度x寬度x通道)大小的圖像'

'計(jì)算10次運(yùn)訓(xùn)時(shí)間的總和') print('CPU (s):')

cpu_time = timeit.timeit('cpu', number=10, setup='from __main__ import cpu') print(cpu_time) print('GPU (s):')

gpu_time = timeit.timeit('gpu', number=10, setup='from __main__ import gpu') print(gpu_time) print('GPU加速超過CPU: {}倍'.format(int(cpu_time/gpu_time)))

sess.close # CPU (s): # 3.593296914000007 # GPU (s): # 0.1831514239999592 # GPU加速超過CPU: 19倍

import numpy as np

def add_op(x, y):

return x + y

x = tf.placeholder(tf.float32, [10,])

y = tf.placeholder(tf.float32, [10,])

tpu_ops = tf.contrib.tpu.rewrite(add_op, [x, y])

session = tf.Session(tpu_address)

try:

print('Initializing...')

session.run(tf.contrib.tpu.initialize_system)

print('Running ops')

print(session.run(tpu_ops, {x: np.arange(10), y: np.arange(10)}))

# [array([ 0., 2., 4., 6., 8., 10., 12., 14., 16., 18.], dtype=float32)]

finally:

# 目前，tpu會(huì)話必須與關(guān)閉會(huì)話分開關(guān)閉。

session.run(tf.contrib.tpu.shutdown_system)

session.close

from __future__ import print_function

import keras

from keras.datasets import mnist

from keras.models import Sequential

from keras.layers import Dense,Dropout,Flatten

from keras.layers import Conv2D,MaxPooling2D

from keras import backend as K

from keras.callbacks import TensorBoard

batch_size = 128

num_classes = 10

epochs = 12

# input image dimensions

img_rows, img_cols = 28,28

# the data, shuffled and split between train and test sets

(x_train, y_train), (x_test, y_test) = mnist.load_data

if K.image_data_format == 'channels_first':

x_train = x_train.reshape(x_train.shape[0],1, img_rows, img_cols)

x_test = x_test.reshape(x_test.shape[0],1, img_rows, img_cols)

input_shape = (1, img_rows, img_cols)

else:

x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols,1)

x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols,1)

input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')

x_test = x_test.astype('float32')

x_train /= 255

x_test /= 255

print('x_train shape:', x_train.shape)

print(x_train.shape[0], 'train samples')

print(x_test.shape[0], 'test samples')

# 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)

model = Sequential

model.add(Conv2D(32, kernel_size=(3,3),

activation='relu',

input_shape=input_shape))

model.add(Conv2D(64, (3,3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Dropout(0.25))

model.add(Flatten)

model.add(Dense(128, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,

optimizer=keras.optimizers.Adadelta,

metrics=['accuracy'])

tbCallBack = TensorBoard(log_dir=LOG_DIR,

histogram_freq=1,

write_graph=True,

write_grads=True,

batch_size=batch_size,

write_images=True)

model.fit(x_train, y_train,

batch_size=batch_size,

epochs=epochs,

verbose=1,

validation_data=(x_test, y_test),

callbacks=[tbCallBack])

score = model.evaluate(x_test, y_test, verbose=0)

print('Test loss:', score[0])

print('Test accuracy:', score[1])