Tensorflow 简明教程

conda create --name tensorflow python = 3.5

activate tensorflow

pip install tensorflow

pip install tensorflow-gpu

>> activate tensorflow
>> python (activating python shell)
>> import tensorflow as tf
>> hello = tf.constant(‘Hello, Tensorflow!’)
>> sess = tf.Session()
>> print(sess.run(hello))

import tensorflow as tf

# Constants creation for TensorBoard visualization
a = tf.constant(10,name = "a")
b = tf.constant(90,name = "b")
y = tf.Variable(a+b*2,name = 'y')
model = tf.initialize_all_variables() #Creation of model

with tf.Session() as session:
   merged = tf.merge_all_summaries()
   writer = tf.train.SummaryWriter("/tmp/tensorflowlogs",session.graph)
   session.run(model)
   print(session.run(y))

blue: (0.01359, 0.00075997, 0.24608, ..., -0.2524, 1.0048, 0.06259)
blues: (0.01396, 0.11887, -0.48963, ..., 0.033483, -0.10007, 0.1158)
orange: (-0.24776, -0.12359, 0.20986, ..., 0.079717, 0.23865, -0.014213)
oranges: (-0.35609, 0.21854, 0.080944, ..., -0.35413, 0.38511, -0.070976)

Y = Ax+b

pip install tflearn

from __future__ import division, print_function, absolute_import

#TFLearn module implementation
import tflearn
from tflearn.estimators import RandomForestClassifier

# Data loading and pre-processing with respect to dataset
import tflearn.datasets.mnist as mnist
X, Y, testX, testY = mnist.load_data(one_hot = False)

m = RandomForestClassifier(n_estimators = 100, max_nodes = 1000)
m.fit(X, Y, batch_size = 10000, display_step = 10)

print("Compute the accuracy on train data:")
print(m.evaluate(X, Y, tflearn.accuracy_op))

print("Compute the accuracy on test set:")
print(m.evaluate(testX, testY, tflearn.accuracy_op))

print("Digits for test images id 0 to 5:")
print(m.predict(testX[:5]))

print("True digits:")
print(testY[:5])

import warnings
warnings.filterwarnings('ignore')

import numpy as np
np.random.seed(123) # for reproducibility

from keras.models import Sequential
from keras.layers import Flatten, MaxPool2D, Conv2D, Dense, Reshape, Dropout
from keras.utils import np_utils
Using TensorFlow backend.
from keras.datasets import mnist

# Load pre-shuffled MNIST data into train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)

model = Sequential()
model.add(Conv2D(32, 3, 3, activation = 'relu', input_shape = (28,28,1)))
model.add(Conv2D(32, 3, 3, activation = 'relu'))
model.add(MaxPool2D(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(10, activation = 'softmax'))

model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])

model.fit(X_train, Y_train, batch_size = 32, epochs = 10, verbose = 1)

Epoch 1/10 60000/60000 [==============================] - 65s -
loss: 0.2124 -
acc: 0.9345
Epoch 2/10 60000/60000 [==============================] - 62s -
loss: 0.0893 -
acc: 0.9740
Epoch 3/10 60000/60000 [==============================] - 58s -
loss: 0.0665 -
acc: 0.9802
Epoch 4/10 60000/60000 [==============================] - 62s -
loss: 0.0571 -
acc: 0.9830
Epoch 5/10 60000/60000 [==============================] - 62s -
loss: 0.0474 -
acc: 0.9855
Epoch 6/10 60000/60000 [==============================] - 59s -
loss: 0.0416 -
acc: 0.9871
Epoch 7/10 60000/60000 [==============================] - 61s -
loss: 0.0380 -
acc: 0.9877
Epoch 8/10 60000/60000 [==============================] - 63s -
loss: 0.0333 -
acc: 0.9895
Epoch 9/10 60000/60000 [==============================] - 64s -
loss: 0.0325 -
acc: 0.9898
Epoch 10/10 60000/60000 [==============================] - 60s -
loss: 0.0284 -
acc: 0.9910

import tensorflow as tf

cluster_spec = tf.train.ClusterSpec({'worker' : ['localhost:2222']})
server = tf.train.Server(cluster_spec)
server.target

'grpc://localhost:2222'
The server is currently running.

server.server_def

cluster {
   job {
      name: "worker"
      tasks {
         value: "localhost:2222"
      }
   }
}
job_name: "worker"
protocol: "grpc"

sess = tf.Session(target = server.target)
server = tf.train.Server.create_local_server()

devices = sess.list_devices()
for d in devices:
   print(d.name)
sess.close()

/job:worker/replica:0/task:0/device:CPU:0

def export_meta_graph(filename = None, collection_list = None, as_text = False):
   """this code writes `MetaGraphDef` to save_path/filename.

   Arguments:
   filename: Optional meta_graph filename including the path. collection_list:
      List of string keys to collect. as_text: If `True`,
      writes the meta_graph as an ASCII proto.

   Returns:
   A `MetaGraphDef` proto. """

# Build the model ...
with tf.Session() as sess:
   # Use the model ...
# Export the model to /tmp/my-model.meta.
meta_graph_def = tf.train.export_meta_graph(filename = '/tmp/my-model.meta')

# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)

import tensorflow as tf
import matplotlib.pyplot as plt

# Parameters
learning_rate = 0.001
training_epochs = 20
batch_size = 100
display_step = 1

# Network Parameters
n_hidden_1 = 256

# 1st layer num features
n_hidden_2 = 256 # 2nd layer num features
n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10
# MNIST total classes (0-9 digits)

# tf Graph input
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])

# weights layer 1
h = tf.Variable(tf.random_normal([n_input, n_hidden_1])) # bias layer 1
bias_layer_1 = tf.Variable(tf.random_normal([n_hidden_1]))
# layer 1 layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, h), bias_layer_1))

# weights layer 2
w = tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]))

# bias layer 2
bias_layer_2 = tf.Variable(tf.random_normal([n_hidden_2]))

# layer 2
layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, w), bias_layer_2))

# weights output layer
output = tf.Variable(tf.random_normal([n_hidden_2, n_classes]))

# biar output layer
bias_output = tf.Variable(tf.random_normal([n_classes])) # output layer
output_layer = tf.matmul(layer_2, output) + bias_output

# cost function
cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
   logits = output_layer, labels = y))

#cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(output_layer, y))
# optimizer
optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)

# optimizer = tf.train.GradientDescentOptimizer(
   learning_rate = learning_rate).minimize(cost)

# Plot settings
avg_set = []
epoch_set = []

# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
with tf.Session() as sess:
   sess.run(init)

   # Training cycle
   for epoch in range(training_epochs):
      avg_cost = 0.
      total_batch = int(mnist.train.num_examples / batch_size)

      # Loop over all batches
      for i in range(total_batch):
         batch_xs, batch_ys = mnist.train.next_batch(batch_size)
         # Fit training using batch data sess.run(optimizer, feed_dict = {
            x: batch_xs, y: batch_ys})
         # Compute average loss
         avg_cost += sess.run(cost, feed_dict = {x: batch_xs, y: batch_ys}) / total_batch
      # Display logs per epoch step
      if epoch % display_step == 0:
         print
         Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost)
      avg_set.append(avg_cost)
      epoch_set.append(epoch + 1)
   print
   "Training phase finished"

   plt.plot(epoch_set, avg_set, 'o', label = 'MLP Training phase')
   plt.ylabel('cost')
   plt.xlabel('epoch')
   plt.legend()
   plt.show()

   # Test model
   correct_prediction = tf.equal(tf.argmax(output_layer, 1), tf.argmax(y, 1))

   # Calculate accuracy
   accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
   print
   "Model Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})

#Importing the necessary modules
import tensorflow as tf
import numpy as np
import math, random
import matplotlib.pyplot as plt

np.random.seed(1000)
function_to_learn = lambda x: np.cos(x) + 0.1*np.random.randn(*x.shape)
layer_1_neurons = 10
NUM_points = 1000

#Training the parameters
batch_size = 100
NUM_EPOCHS = 1500

all_x = np.float32(np.random.uniform(-2*math.pi, 2*math.pi, (1, NUM_points))).T
   np.random.shuffle(all_x)

train_size = int(900)
#Training the first 700 points in the given set x_training = all_x[:train_size]
y_training = function_to_learn(x_training)

#Training the last 300 points in the given set x_validation = all_x[train_size:]
y_validation = function_to_learn(x_validation)

plt.figure(1)
plt.scatter(x_training, y_training, c = 'blue', label = 'train')
plt.scatter(x_validation, y_validation, c = 'pink', label = 'validation')
plt.legend()
plt.show()

X = tf.placeholder(tf.float32, [None, 1], name = "X")
Y = tf.placeholder(tf.float32, [None, 1], name = "Y")

#first layer
#Number of neurons = 10
w_h = tf.Variable(
   tf.random_uniform([1, layer_1_neurons],\ minval = -1, maxval = 1, dtype = tf.float32))
b_h = tf.Variable(tf.zeros([1, layer_1_neurons], dtype = tf.float32))
h = tf.nn.sigmoid(tf.matmul(X, w_h) + b_h)

#output layer
#Number of neurons = 10
w_o = tf.Variable(
   tf.random_uniform([layer_1_neurons, 1],\ minval = -1, maxval = 1, dtype = tf.float32))
b_o = tf.Variable(tf.zeros([1, 1], dtype = tf.float32))

#build the model
model = tf.matmul(h, w_o) + b_o

#minimize the cost function (model - Y)
train_op = tf.train.AdamOptimizer().minimize(tf.nn.l2_loss(model - Y))

#Start the Learning phase
sess = tf.Session() sess.run(tf.initialize_all_variables())

errors = []
for i in range(NUM_EPOCHS):
   for start, end in zip(range(0, len(x_training), batch_size),\
      range(batch_size, len(x_training), batch_size)):
      sess.run(train_op, feed_dict = {X: x_training[start:end],\ Y: y_training[start:end]})
   cost = sess.run(tf.nn.l2_loss(model - y_validation),\ feed_dict = {X:x_validation})
   errors.append(cost)

   if i%100 == 0:
      print("epoch %d, cost = %g" % (i, cost))

plt.plot(errors,label='MLP Function Approximation') plt.xlabel('epochs')
plt.ylabel('cost')
plt.legend()
plt.show()

tf.train.Optimizer

def sgd(cost, params, lr = np.float32(0.01)):
   g_params = tf.gradients(cost, params)
   updates = []

   for param, g_param in zip(params, g_params):
      updates.append(param.assign(param - lr*g_param))
   return updates

#Declaring necessary modules
import tensorflow as tf
import numpy as np
"""
A simple numpy implementation of a XOR gate to understand the backpropagation
algorithm
"""

x = tf.placeholder(tf.float64,shape = [4,2],name = "x")
#declaring a place holder for input x
y = tf.placeholder(tf.float64,shape = [4,1],name = "y")
#declaring a place holder for desired output y

m = np.shape(x)[0]#number of training examples
n = np.shape(x)[1]#number of features
hidden_s = 2 #number of nodes in the hidden layer
l_r = 1#learning rate initialization

theta1 = tf.cast(tf.Variable(tf.random_normal([3,hidden_s]),name = "theta1"),tf.float64)
theta2 = tf.cast(tf.Variable(tf.random_normal([hidden_s+1,1]),name = "theta2"),tf.float64)

#conducting forward propagation
a1 = tf.concat([np.c_[np.ones(x.shape[0])],x],1)
#the weights of the first layer are multiplied by the input of the first layer

z1 = tf.matmul(a1,theta1)
#the input of the second layer is the output of the first layer, passed through the
   activation function and column of biases is added

a2 = tf.concat([np.c_[np.ones(x.shape[0])],tf.sigmoid(z1)],1)
#the input of the second layer is multiplied by the weights

z3 = tf.matmul(a2,theta2)
#the output is passed through the activation function to obtain the final probability

h3 = tf.sigmoid(z3)
cost_func = -tf.reduce_sum(y*tf.log(h3)+(1-y)*tf.log(1-h3),axis = 1)

#built in tensorflow optimizer that conducts gradient descent using specified
   learning rate to obtain theta values

optimiser = tf.train.GradientDescentOptimizer(learning_rate = l_r).minimize(cost_func)

#setting required X and Y values to perform XOR operation
X = [[0,0],[0,1],[1,0],[1,1]]
Y = [[0],[1],[1],[0]]

#initializing all variables, creating a session and running a tensorflow session
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

#running gradient descent for each iteration and printing the hypothesis
   obtained using the updated theta values
for i in range(100000):
   sess.run(optimiser, feed_dict = {x:X,y:Y})#setting place holder values using feed_dict
   if i%100==0:
      print("Epoch:",i)
      print("Hyp:",sess.run(h3,feed_dict = {x:X,y:Y}))

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def make_kernel(a):
   a = np.asarray(a)
   a = a.reshape(list(a.shape) + [1,1])
   return tf.constant(a, dtype=1)

def simple_conv(x, k):
   """A simplified 2D convolution operation"""
   x = tf.expand_dims(tf.expand_dims(x, 0), -1)
   y = tf.nn.depthwise_conv2d(x, k, [1, 1, 1, 1], padding = 'SAME')
   return y[0, :, :, 0]

def laplace(x):
   """Compute the 2D laplacian of an array"""
   laplace_k = make_kernel([[0.5, 1.0, 0.5], [1.0, -6., 1.0], [0.5, 1.0, 0.5]])
   return simple_conv(x, laplace_k)

sess = tf.InteractiveSession()

N = 500

# Initial Conditions -- some rain drops hit a pond

# Set everything to zero
u_init = np.zeros([N, N], dtype = np.float32)
ut_init = np.zeros([N, N], dtype = np.float32)

# Some rain drops hit a pond at random points
for n in range(100):
   a,b = np.random.randint(0, N, 2)
   u_init[a,b] = np.random.uniform()

plt.imshow(u_init)
plt.show()

# Parameters:
# eps -- time resolution
# damping -- wave damping
eps = tf.placeholder(tf.float32, shape = ())
damping = tf.placeholder(tf.float32, shape = ())

# Create variables for simulation state
U = tf.Variable(u_init)
Ut = tf.Variable(ut_init)

# Discretized PDE update rules
U_ = U + eps * Ut
Ut_ = Ut + eps * (laplace(U) - damping * Ut)

# Operation to update the state
step = tf.group(U.assign(U_), Ut.assign(Ut_))

# Initialize state to initial conditions
tf.initialize_all_variables().run()

# Run 1000 steps of PDE
for i in range(1000):
   # Step simulation
   step.run({eps: 0.03, damping: 0.04})

   # Visualize every 50 steps
   if i % 500 == 0:
      plt.imshow(U.eval())
      plt.show()

import pickle
from sklearn.model_selection import train_test_split
from scipy import misc

import numpy as np
import os

label = os.listdir("dataset_image")
label = label[1:]
dataset = []

for image_label in label:
   images = os.listdir("dataset_image/"+image_label)

   for image in images:
      img = misc.imread("dataset_image/"+image_label+"/"+image)
      img = misc.imresize(img, (64, 64))
      dataset.append((img,image_label))
X = []
Y = []

for input,image_label in dataset:
   X.append(input)
   Y.append(label.index(image_label))

X = np.array(X)
Y = np.array(Y)

X_train,y_train, = X,Y

data_set = (X_train,y_train)

save_label = open("int_to_word_out.pickle","wb")
pickle.dump(label, save_label)
save_label.close()

import numpy
import matplotlib.pyplot as plt

from keras.layers import Dropout
from keras.layers import Flatten
from keras.constraints import maxnorm
from keras.optimizers import SGD
from keras.layers import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K

import load_data
from keras.models import Sequential
from keras.layers import Dense

import keras
K.set_image_dim_ordering('tf')

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)

# load data
(X_train,y_train) = load_data.data_set

# normalize inputs from 0-255 to 0.0-1.0
X_train = X_train.astype('float32')

#X_test = X_test.astype('float32')
X_train = X_train / 255.0

#X_test = X_test / 255.0
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)

#y_test = np_utils.to_categorical(y_test)
num_classes = y_train.shape[1]

# Create the model
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), padding = 'same',
   activation = 'relu', kernel_constraint = maxnorm(3)))

model.add(Dropout(0.2))
model.add(Conv2D(32, (3, 3), activation = 'relu', padding = 'same',
   kernel_constraint = maxnorm(3)))

model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Flatten())
model.add(Dense(512, activation = 'relu', kernel_constraint = maxnorm(3)))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation = 'softmax'))

# Compile model
epochs = 10
lrate = 0.01
decay = lrate/epochs
sgd = SGD(lr = lrate, momentum = 0.9, decay = decay, nesterov = False)
model.compile(loss = 'categorical_crossentropy', optimizer = sgd, metrics = ['accuracy'])
print(model.summary())

#callbacks = [keras.callbacks.EarlyStopping(
   monitor = 'val_loss', min_delta = 0, patience = 0, verbose = 0, mode = 'auto')]
callbacks = [keras.callbacks.TensorBoard(log_dir='./logs',
   histogram_freq = 0, batch_size = 32, write_graph = True, write_grads = False,
   write_images = True, embeddings_freq = 0, embeddings_layer_names = None,
   embeddings_metadata = None)]

# Fit the model

model.fit(X_train, y_train, epochs = epochs,
   batch_size = 32,shuffle = True,callbacks = callbacks)

# Final evaluation of the model
scores = model.evaluate(X_train, y_train, verbose = 0)
print("Accuracy: %.2f%%" % (scores[1]*100))

# serialize model to JSONx
model_json = model.to_json()
with open("model_face.json", "w") as json_file:
   json_file.write(model_json)

# serialize weights to HDF5
model.save_weights("model_face.h5")
print("Saved model to disk")