本人首先安裝的是anaconda。
安裝完anaconda，我們可以修改鏡像源，提升下載速度。這裏使用中科大的：
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes
1，安裝tensorflow-gpu和keras
1.1 創建環境，環境名爲keras（因爲這個tensorflow作爲keras的基礎），並指定python版本
conda create -n keras python=3.6
1.2 激活環境，安裝tensorflow-gpu，順便安裝keras
activate keras
pip install --ignore-installed --upgrade tensorflow-gpu
pip install keras
1.3 如果keras底層不是tensorflow而是其他，在文件目錄框輸入%USERPROFILE%搜索，然後搜索keras.json這個文件，修改backend屬性爲其他值即可。
1.4 驗證keras安裝成功，輸出tensorflow即爲成功。
import keras
print(keras.backend.backend())
2，因爲tensorflow-gpu版需要cuda（平臺支持）和cudnn（計算加速）的支持，下面開始繼續安裝：
2.1 檢查電腦顯卡型號能夠支持的最大cuda版本：網址爲顯卡型號對應cuda版本
顯卡型號及內存模式（如ddr Gddr等）可以使用gpu-z軟件查看。比如我是417.22，那麼最高可以下載cuda10.0版本
2.2 cuda下載網址：cuda10.0下載
2.3 cuda安裝，選擇自定義安裝，cuda必要，GeForce Experience可以不要，其他2個看是否新版本大於當前版本，否則也不必要。安裝完成後將cuda的bin目錄加入環境變量中。然後在控制檯輸入nvcc --version驗證安裝成功。
2.4 cudnn下載網址：cudnn下載
cudnn要看cuda的版本，如cuda10.0，那麼cudnn可以是7.5。這裏需要註冊，不過很簡單，下載完成後解壓，將3個文件夾裏的文件拷貝至cuda的同名文件夾下即可。
3 檢查tensorflow-gpu是否可用
在控制檯下輸入，將會顯示顯卡信息。
import tensorflow as tf
sess=tf.Session(config=tf.ConfigProto(log_device_placement=True))
4 如果同時安裝了cpu和gpu版tensorflow，默認使用gpu版，切換如下：
with tf.Session() as sess:
with tf.device("/gpu:0"):#指定gpu或cpu
dosth...
5 最後，cpu與gpu比較，使用mnist數據集測試
代碼如下：
"""Convolutional Neural Network Estimator for MNIST, built with tf.layers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.INFO)
def cnn_model_fn(features, labels, mode):
"""Model function for CNN."""
# Input Layer
# Reshape X to 4-D tensor: [batch_size, width, height, channels]
# MNIST images are 28x28 pixels, and have one color channel
input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
# Convolutional Layer #1
# Computes 32 features using a 5x5 filter with ReLU activation.
# Padding is added to preserve width and height.
# Input Tensor Shape: [batch_size, 28, 28, 1]
# Output Tensor Shape: [batch_size, 28, 28, 32]
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #1
# First max pooling layer with a 2x2 filter and stride of 2
# Input Tensor Shape: [batch_size, 28, 28, 32]
# Output Tensor Shape: [batch_size, 14, 14, 32]
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
# Convolutional Layer #2
# Computes 64 features using a 5x5 filter.
# Padding is added to preserve width and height.
# Input Tensor Shape: [batch_size, 14, 14, 32]
# Output Tensor Shape: [batch_size, 14, 14, 64]
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #2
# Second max pooling layer with a 2x2 filter and stride of 2
# Input Tensor Shape: [batch_size, 14, 14, 64]
# Output Tensor Shape: [batch_size, 7, 7, 64]
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
# Flatten tensor into a batch of vectors
# Input Tensor Shape: [batch_size, 7, 7, 64]
# Output Tensor Shape: [batch_size, 7 * 7 * 64]
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
# Dense Layer
# Densely connected layer with 1024 neurons
# Input Tensor Shape: [batch_size, 7 * 7 * 64]
# Output Tensor Shape: [batch_size, 1024]
dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
# Add dropout operation; 0.6 probability that element will be kept
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
# Logits layer
# Input Tensor Shape: [batch_size, 1024]
# Output Tensor Shape: [batch_size, 10]
logits = tf.layers.dense(inputs=dropout, units=10)
predictions = {
# Generate predictions (for PREDICT and EVAL mode)
"classes": tf.argmax(input=logits, axis=1),
# Add `softmax_tensor` to the graph. It is used for PREDICT and by the
# `logging_hook`.
"probabilities": tf.nn.softmax(logits, name="softmax_tensor")
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Calculate Loss (for both TRAIN and EVAL modes)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Configure the Training Op (for TRAIN mode)
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(
loss=loss,
global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
# Add evaluation metrics (for EVAL mode)
eval_metric_ops = {
"accuracy": tf.metrics.accuracy(
labels=labels, predictions=predictions["classes"])}
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
def main(unused_argv):
# Load training and eval data
mnist = tf.contrib.learn.datasets.load_dataset("mnist")
train_data = mnist.train.images # Returns np.array
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images # Returns np.array
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
# Create the Estimator
mnist_classifier = tf.estimator.Estimator(
model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
# Set up logging for predictions
# Log the values in the "Softmax" tensor with label "probabilities"
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(
tensors=tensors_to_log, every_n_iter=50)
# Train the model
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": train_data},
y=train_labels,
batch_size=100,
num_epochs=None,
shuffle=True)
mnist_classifier.train(
input_fn=train_input_fn,
steps=20000,
hooks=[logging_hook])
# Evaluate the model and print results
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": eval_data},
y=eval_labels,
num_epochs=1,
shuffle=False)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)
if __name__ == "__main__":
tf.app.run()
使用cpu版時，大概每一批（100個）數據訓練需要17s左右，使用gpu版（且我的筆記本顯卡爲GeForce 940MX，相當low了）時，每一批訓練需要2.5s左右。可見速度還是有不少提升的。