软培
機器學習的概念
有 監 督 學 習(Supervised Learning)：
\[x, y\]
輸入高維向量（Vector）并同時輸入其標簽（Label），通過建模（Modeling），使得機器能夠自動在模型中計算最為合適的參數值（Parameter）。最終，使得我們獲得一個參數確定的模型，并在未來輸入新的高維向量時可以預測其Label值，使得Label的誤差在所允許的范圍之內。
無 監 督 學 習(Non-Supervised Learning)：
\[x\]
輸入高維向量（Vector）并，通過建模（Modeling），使得機器能夠計算出模型中相應的待定參數值（Parameter）。
典型的監督學習
貝葉斯概率
概率是一種事情發生可能性的描述.
1.1 重要原則：
- 結論來源于觀測
- 定量描述優于定性描述
- 結論條件明確，敘述簡潔，有好的泛化性
- 高度自洽性，高度可驗證性
1.2 古典概型（靜態概型）
- 試驗只有有限個基本結果
- 試驗的每個基本結果出現的可能性是一樣的
1.3 基于統計的概率
- 在一定條件下，重復做\(n\)次試驗（對一個事情觀測\(n\)次），一個事件\(A\)發生的概率\(P(A)\)
\[P(A) = \frac{A發生次數}{總觀測次數}\]
- \(A與B正相關, P(A)<P(A|B)\)
- \(A與B負相關, P(A)>P(A|B)\)
樸素貝葉斯分類器
維基百科:所有樸素貝葉斯分類器都假定樣本每個特征與其他特征都不相關。
一個普通的規則就是選出最有可能的那個：這就是大家熟知的最大后驗概率（MAP）決策準則。
\[classify\{f_1,\dots,f_n\} = \underset{c}{\operatorname{argmax}} \ p(C=c) \displaystyle\prod_{i=1}^n p(F_i=f_i\vert C=c)\]
高斯樸素貝葉斯
如果要處理的是連續數據一種通常的假設是這些連續數值為高斯分布。
例如，假設訓練集中有一個連續屬性，\(x\)。我們首先對數據根據類別分類，然后計算每個類別中\(x\)的均值和方差。令\(\mu_c\)表示為\(x\)在''c''類上的均值，令\(\sigma^2_c\)為 \(x\)在''c''類上的方差。在給定類中某個值的概率，\(P(x=v|c)\)，可以通過將\(v\)表示為均值為\(\mu_c\)方差為\(\sigma^2_c\)正態分布計算出來。如下，
\[ P(x=v|c)=\tfrac{1}{\sqrt{2\pi\sigma^2_c}}\,e^{ -\frac{(v-\mu_c)^2}{2\sigma^2_c} } \]
處理連續數值問題的另一種常用的技術是通過離散化連續數值的方法。通常，當訓練樣本數量較少或者是精確的分布已知時，通過概率分布的方法是一種更好的選擇。在大量樣本的情形下離散化的方法表現更優，因為大量的樣本可以學習到數據的分布。由于樸素貝葉斯是一種典型的用到大量樣本的方法（越大計算量的模型可以產生越高的分類精確度），所以樸素貝葉斯方法都用到離散化方法，而不是概率分布估計的方法。
hello.py
import tensorflow as tf hello = tf.constant('Hello, TensorFlow!') sess = tf.Session() print(sess.run(hello)) a = tf.constant(10) b = tf.constant(32) print(sess.run(a + b)) b'Hello, TensorFlow!' 42實驗指導：
實現一個線性回歸的例子
# ex-01.py import tensorflow as tf import numpy as np#create some training data x_data = np.random.rand(100).astype(np.float32) y_data = x_data*1 + 3print ("x_data:") print (x_data)print ("y_data:") print( y_data)#create the weights and bias variables weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0)) print ("weights before initializing:") print (weights)biases = tf.Variable(tf.zeros([1])) print ("bias before initializing:") print (biases)#predict (fit) value y = weights*x_data + biases#loss function loss = tf.reduce_mean(tf.square(y - y_data))#optimizer definition optimizer = tf.train.GradientDescentOptimizer(0.1)#train definition train = optimizer.minimize(loss)#initialiing the variables init = tf.initialize_all_variables()#session definition and active sess = tf.Session() sess.run(init)#train the model for step in range(501):sess.run(train)if step % 10 == 0:print (step,sess.run(weights),sess.run(biases)) x_data: [ 0.89402199 0.10053575 0.55701882 0.53727293 0.36683112 0.442012130.42864946 0.33498201 0.55696607 0.40338814 0.99002725 0.233939480.40767717 0.20491761 0.10732751 0.64065552 0.95823038 0.369610490.43446437 0.05964461 0.39571118 0.36884421 0.92716092 0.859387990.61906868 0.28850925 0.33652243 0.34363723 0.19535853 0.122284320.87395531 0.94348276 0.58827281 0.42699504 0.68471229 0.491384240.60810924 0.59445798 0.48714778 0.84556079 0.71666372 0.730189740.45137393 0.89610088 0.23163974 0.57911086 0.59919071 0.146428320.16170129 0.93183321 0.03960106 0.02113333 0.26829427 0.054383320.26300624 0.25510544 0.47461808 0.42821345 0.13820758 0.617614450.95918888 0.57477629 0.80746269 0.9087519 0.29300612 0.22989190.47188538 0.76722085 0.5297811 0.20937359 0.86967862 0.110506950.41093281 0.43827012 0.63228905 0.29376149 0.35848793 0.443831120.11717488 0.82729691 0.06545471 0.2652818 0.00641522 0.543565930.69357717 0.9950943 0.72574335 0.77202976 0.6673649 0.336639970.93204492 0.86043173 0.88384038 0.27974007 0.33629051 0.649073240.77879322 0.63549143 0.0160666 0.56776083] y_data: [ 3.89402199 3.10053587 3.55701876 3.53727293 3.36683106 3.442012073.42864943 3.33498192 3.55696607 3.40338802 3.99002719 3.233939413.40767717 3.20491767 3.10732746 3.64065552 3.9582305 3.369610553.43446445 3.0596447 3.39571118 3.36884427 3.92716098 3.859387873.61906862 3.28850937 3.33652234 3.34363723 3.19535851 3.122284413.87395525 3.94348288 3.58827281 3.42699504 3.68471241 3.491384273.60810924 3.5944581 3.48714781 3.84556079 3.71666384 3.73018983.45137405 3.896101 3.23163986 3.57911086 3.59919071 3.146428353.1617012 3.93183327 3.03960109 3.02113342 3.26829433 3.054383283.26300621 3.2551055 3.47461796 3.42821336 3.13820767 3.617614513.95918894 3.57477617 3.80746269 3.90875196 3.29300618 3.229891783.47188544 3.76722097 3.5297811 3.20937347 3.8696785 3.110507013.41093278 3.43827009 3.63228893 3.29376149 3.35848784 3.443831213.11717486 3.82729697 3.06545472 3.26528168 3.00641513 3.543565993.69357729 3.9950943 3.72574329 3.77202988 3.66736484 3.336639883.93204498 3.86043167 3.88384032 3.2797401 3.3362906 3.649073123.77879333 3.63549137 3.01606655 3.56776094] weights before initializing: <tf.Variable 'Variable:0' shape=(1,) dtype=float32_ref> bias before initializing: <tf.Variable 'Variable_1:0' shape=(1,) dtype=float32_ref> WARNING:tensorflow:From C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\util\tf_should_use.py:170: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02. Instructions for updating: Use `tf.global_variables_initializer` instead. 0 [ 0.90101707] [ 0.64157164] 10 [ 1.73624611] [ 2.48117089] 20 [ 1.6977222] [ 2.62674093] 30 [ 1.62073231] [ 2.67402244] 40 [ 1.55015469] [ 2.7114203] 50 [ 1.48748767] [ 2.74431014] 60 [ 1.43195248] [ 2.77343965] 70 [ 1.3827436] [ 2.79925013] 80 [ 1.33914053] [ 2.82212019] 90 [ 1.30050492] [ 2.84238434] 100 [ 1.266271] [ 2.86034036] 110 [ 1.23593676] [ 2.87625074] 120 [ 1.2090584] [ 2.8903482] 130 [ 1.18524206] [ 2.9028399] 140 [ 1.16413891] [ 2.91390872] 150 [ 1.14543998] [ 2.92371631] 160 [ 1.12887108] [ 2.93240666] 170 [ 1.11418998] [ 2.94010711] 180 [ 1.10118115] [ 2.94693041] 190 [ 1.08965421] [ 2.95297623] 200 [ 1.07944047] [ 2.95833325] 210 [ 1.07039058] [ 2.96307993] 220 [ 1.06237149] [ 2.96728611] 230 [ 1.0552659] [ 2.97101283] 240 [ 1.04896998] [ 2.97431517] 250 [ 1.04339111] [ 2.97724128] 260 [ 1.03844786] [ 2.97983384] 270 [ 1.03406775] [ 2.98213148] 280 [ 1.03018677] [ 2.9841671] 290 [ 1.0267477] [ 2.98597097] 300 [ 1.02370059] [ 2.98756886] 310 [ 1.02100062] [ 2.98898506] 320 [ 1.01860821] [ 2.99023986] 330 [ 1.01648819] [ 2.99135184] 340 [ 1.01460981] [ 2.99233723] 350 [ 1.01294541] [ 2.99321008] 360 [ 1.01147056] [ 2.99398375] 370 [ 1.01016378] [ 2.99466896] 380 [ 1.00900602] [ 2.99527621] 390 [ 1.00797999] [ 2.99581456] 400 [ 1.00707078] [ 2.9962914] 410 [ 1.00626528] [ 2.99671388] 420 [ 1.00555134] [ 2.99708843] 430 [ 1.00491893] [ 2.99742007] 440 [ 1.00435853] [ 2.99771404] 450 [ 1.0038619] [ 2.9979744] 460 [ 1.00342214] [ 2.99820518] 470 [ 1.00303233] [ 2.99840927] 480 [ 1.00268698] [ 2.99859095] 490 [ 1.00238085] [ 2.9987514] 500 [ 1.00210965] [ 2.9988935]實現一個矩陣乘法計算
# ex-02.py import tensorflow as tfmatrix1 = tf.constant([[3,3]])matrix2 = tf.constant([[1],[2]])#matrix multiply product = tf.matmul(matrix1, matrix2)#method1 #sess = tf.Session() #result = sess.run(product) #print "result:",result #sess.close()#method2 with tf.Session() as sess:result2 = sess.run(product)print ("result2:",result2) result2: [[9]]常量/變量/賦值
# ex-03.py import tensorflow as tfstate = tf.Variable(10, name='counter')print( state.name)one = tf.constant(1)new_value = tf.add(state, one)update = tf.assign(state, new_value)#this initializing is very important init = tf.global_variables_initializer()with tf.Session() as sess:sess.run(init)for x in range(10):sess.run(update)# must do sess.run to operate the variablesprint(sess.run(state)) counter_1:0 11 12 13 14 15 16 17 18 19 20Placeholder用法
# ex-04.py import tensorflow as tfinput1 = tf.placeholder(tf.float32) input2 = tf.placeholder(tf.float32)output = input1 * input2with tf.Session() as sess:print(sess.run(output, feed_dict={input1: 4, input2: 2}))print(sess.run(output, feed_dict={input1: [4,2], input2: [2,7]})) 8.0 [ 8. 14.] # ex-05.py import tensorflow as tf import numpy as npdef add_layer(inputs, in_size, out_size, activation_function=None):weights = tf.Variable(tf.random_normal([in_size, out_size]))biases = tf.Variable(tf.zeros([1,out_size]))wx_b = tf.matmul(inputs, weights) + biasesif activation_function is None:outputs = wx_belse:outputs = activation_function(wx_b)return outputs#make some input value x_data = np.linspace(-1, 1, 300)[:, np.newaxis] noise = np.random.normal(0, 0.05, x_data.shape) y_data = np.square(x_data) + noisexs = tf.placeholder(tf.float32, [None,1]) ys = tf.placeholder(tf.float32, [None,1])layer1 = add_layer(xs, 1, 10, activation_function=tf.nn.sigmoid) prediction = add_layer(layer1, 10, 1, activation_function=None)loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))train = tf.train.GradientDescentOptimizer(0.01).minimize(loss)init = tf.global_variables_initializer()sess = tf.Session() sess.run(init)for x in range(1000):sess.run(train, feed_dict = { xs: x_data, ys: y_data})if x % 10==0:print (sess.run(loss, feed_dict={xs:x_data, ys: y_data})) 1.84078 0.633829 0.382142 0.309708 0.274133 0.248449 0.227182 0.208942 0.19316 0.179466 0.167569 0.157221 0.148213 0.140364 0.13352 0.127546 0.122329 0.117768 0.113778 0.110283 0.107221 0.104534 0.102175 0.100101 0.0982751 0.0966661 0.095246 0.0939907 0.0928792 0.0918932 0.0910167 0.0902358 0.0895383 0.0889138 0.0883529 0.0878477 0.087391 0.0869768 0.0865996 0.0862548 0.0859384 0.0856467 0.0853765 0.0851253 0.0848907 0.0846704 0.0844628 0.0842661 0.0840792 0.0839006 0.0837294 0.0835647 0.0834056 0.0832514 0.0831015 0.0829553 0.0828125 0.0826725 0.082535 0.0823997 0.0822664 0.0821347 0.0820044 0.0818755 0.0817476 0.0816208 0.0814948 0.0813695 0.0812449 0.0811209 0.0809973 0.0808742 0.0807514 0.080629 0.0805069 0.080385 0.0802633 0.0801419 0.0800206 0.0798994 0.0797783 0.0796574 0.0795365 0.0794157 0.079295 0.0791743 0.0790537 0.0789332 0.0788126 0.0786921 0.0785716 0.0784511 0.0783306 0.0782102 0.0780897 0.0779693 0.0778488 0.0777284 0.0776079 0.0774875可視化
tensorboard --logdir='logs/'
注意：不能在notebook直接運行。
ex-06-vis.py
import time import tensorflow as tf import numpy as np import matplotlib.pyplot as pltdef add_layer(inputs, in_size, out_size, activation_function=None):weights = tf.Variable(tf.random_normal([in_size, out_size]))biases = tf.Variable(tf.zeros([1,out_size]))wx_b = tf.matmul(inputs, weights) + biasesif activation_function is None:outputs = wx_belse:outputs = activation_function(wx_b)return outputs#make some input value x_data = np.linspace(-1, 1, 300)[:, np.newaxis] noise = np.random.normal(0, 0.05, x_data.shape) y_data = np.square(x_data) + noisexs = tf.placeholder(tf.float32, [None,1]) ys = tf.placeholder(tf.float32, [None,1])layer1 = add_layer(xs, 1, 10, activation_function=tf.nn.sigmoid) prediction = add_layer(layer1, 10, 1, activation_function=None)loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))train = tf.train.GradientDescentOptimizer(0.1).minimize(loss)init = tf.global_variables_initializer()sess = tf.Session() sess.run(init)#added coding fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.scatter(x_data, y_data) plt.ion() plt.show()for x in range(1000):sess.run(train, feed_dict = { xs: x_data, ys: y_data})if x % 10==0:# print (sess.run(loss, feed_dict={xs:x_data, ys: y_data}))try:ax.lines.remove(lines[0])except Exception:passprediction_value = sess.run(prediction, feed_dict={xs:x_data})lines = ax.plot(x_data, prediction_value, 'r-', lw=5)plt.pause(0.1) import time for i in [1,2,34,5]:print(i)time.sleep(0.1) 1 2 34 5ex-09-tb.py
# from ex-05 import tensorflow as tf import numpy as npdef add_layer(inputs, in_size, out_size, n_layer, activation_function=None):layer_name = 'layer%s' % n_layerwith tf.name_scope('layer'):with tf.name_scope('weights'):weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')tf.summary.histogram(layer_name + '/weights', weights)with tf.name_scope('bias'):biases = tf.Variable(tf.zeros([1,out_size]), name='b')tf.summary.histogram(layer_name + '/biases', biases)with tf.name_scope('wx_plus_b'):wx_b = tf.add(tf.matmul(inputs, weights), biases)if activation_function is None:outputs = wx_belse:outputs = activation_function(wx_b, name='output')tf.summary.histogram(layer_name + '/outputs', outputs)return outputs#make some input value x_data = np.linspace(-1, 1, 300)[:, np.newaxis] noise = np.random.normal(0, 0.05, x_data.shape) y_data = np.square(x_data) + noisewith tf.name_scope('inputs'):xs = tf.placeholder(tf.float32, [None,1], name='x_input')ys = tf.placeholder(tf.float32, [None,1], name='y_input')layer1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.sigmoid) prediction = add_layer(layer1, 10, 1, n_layer=2, activation_function=None)with tf.name_scope('loss'):loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))tf.summary.scalar('loss', loss)with tf.name_scope('train'):train = tf.train.GradientDescentOptimizer(0.1).minimize(loss)init = tf.global_variables_initializer()sess = tf.Session() sess.run(init)merged = tf.summary.merge_all()writer = tf.summary.FileWriter("logs/", sess.graph)for x in range(50000):sess.run(train, feed_dict = { xs: x_data, ys: y_data})if x % 50==0:sess.run(loss, feed_dict={xs:x_data, ys:y_data})result = sess.run(merged, feed_dict={xs:x_data, ys:y_data})writer.add_summary(result, x) import tensorflow as tfa = tf.constant(5, name="input_a") b = tf.constant(3, name="input_b") c = tf.multiply(a, b, name="mul_c") d = tf.add(a, b, name="add_d") e = tf.add(c, d, name="add_e")sess = tf.Session() sess.run(e)writer = tf.summary.FileWriter("E:/tensorflow/graph", tf.get_default_graph()) writer.close()ex-10.py
import tensorflow as tf import gzip import numpy import collections from tensorflow.python.framework import random_seed from tensorflow.python.framework import dtypes #from tensorflow.examples.tutorials.mnist import input_dataDatasets = collections.namedtuple('Datasets', ['train', 'validation', 'test'])def _read32(bytestream):dt = numpy.dtype(numpy.uint32).newbyteorder('>')return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]def dense_to_one_hot(labels_dense, num_classes):"""Convert class labels from scalars to one-hot vectors."""num_labels = labels_dense.shape[0]index_offset = numpy.arange(num_labels) * num_classeslabels_one_hot = numpy.zeros((num_labels, num_classes))labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1return labels_one_hotdef extract_images(f):"""Extract the images into a 4D uint8 numpy array [index, y, x, depth].Args:f: A file object that can be passed into a gzip reader.Returns:data: A 4D uint8 numpy array [index, y, x, depth].Raises:ValueError: If the bytestream does not start with 2051."""print('Extracting', f.name)with gzip.GzipFile(fileobj=f) as bytestream:magic = _read32(bytestream)if magic != 2051:raise ValueError('Invalid magic number %d in MNIST image file: %s' %(magic, f.name))num_images = _read32(bytestream)rows = _read32(bytestream)cols = _read32(bytestream)buf = bytestream.read(rows * cols * num_images)data = numpy.frombuffer(buf, dtype=numpy.uint8)data = data.reshape(num_images, rows, cols, 1)return datadef extract_labels(f, one_hot=False, num_classes=10):"""Extract the labels into a 1D uint8 numpy array [index].Args:f: A file object that can be passed into a gzip reader.one_hot: Does one hot encoding for the result.num_classes: Number of classes for the one hot encoding.Returns:labels: a 1D uint8 numpy array.Raises:ValueError: If the bystream doesn't start with 2049."""print('Extracting', f.name)with gzip.GzipFile(fileobj=f) as bytestream:magic = _read32(bytestream)if magic != 2049:raise ValueError('Invalid magic number %d in MNIST label file: %s' %(magic, f.name))num_items = _read32(bytestream)buf = bytestream.read(num_items)labels = numpy.frombuffer(buf, dtype=numpy.uint8)if one_hot:return dense_to_one_hot(labels, num_classes)return labelsdef read_data_sets(train_dir,fake_data=False,one_hot=False,dtype=tf.float32,reshape=True,validation_size=5000,seed=None):'''if fake_data:def fake():return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype, seed=seed)train = fake()validation = fake()test = fake()return base.Datasets(train=train, validation=validation, test=test)'''TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'TEST_IMAGES = 't10k-images-idx3-ubyte.gz'TEST_LABELS = 't10k-labels-idx1-ubyte.gz'local_file = '/home/gao/dl_learning/py-example/MNIST_data/' + TRAIN_IMAGESwith open(local_file, 'rb') as f:train_images = extract_images(f)local_file = '/home/gao/dl_learning/py-example/MNIST_data/' + TRAIN_LABELSwith open(local_file, 'rb') as f:train_labels = extract_labels(f, one_hot=one_hot)local_file = '/home/gao/dl_learning/py-example/MNIST_data/' + TEST_IMAGESwith open(local_file, 'rb') as f:test_images = extract_images(f)local_file = '/home/gao/dl_learning/py-example/MNIST_data/' + TEST_LABELSwith open(local_file, 'rb') as f:test_labels = extract_labels(f, one_hot=one_hot)if not 0 <= validation_size <= len(train_images):raise ValueError('Validation size should be between 0 and {}. Received: {}.'.format(len(train_images), validation_size))validation_images = train_images[:validation_size]validation_labels = train_labels[:validation_size]train_images = train_images[validation_size:]train_labels = train_labels[validation_size:]options = dict(dtype=dtype, reshape=reshape, seed=seed)train = DataSet(train_images, train_labels, **options)validation = DataSet(validation_images, validation_labels, **options)test = DataSet(test_images, test_labels, **options)return Datasets(train=train, validation=validation, test=test)class DataSet(object):def __init__(self,images,labels,fake_data=False,one_hot=False,dtype=tf.float32,reshape=True,seed=None):"""Construct a DataSet.one_hot arg is used only if fake_data is true. `dtype` can be either`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into`[0, 1]`. Seed arg provides for convenient deterministic testing."""seed1, seed2 = random_seed.get_seed(seed)# If op level seed is not set, use whatever graph level seed is returnednumpy.random.seed(seed1 if seed is None else seed2)dtype = dtypes.as_dtype(dtype).base_dtypeif dtype not in (dtypes.uint8, dtypes.float32):raise TypeError('Invalid image dtype %r, expected uint8 or float32' %dtype)if fake_data:self._num_examples = 10000self.one_hot = one_hotelse:assert images.shape[0] == labels.shape[0], ('images.shape: %s labels.shape: %s' % (images.shape, labels.shape))self._num_examples = images.shape[0]# Convert shape from [num examples, rows, columns, depth]# to [num examples, rows*columns] (assuming depth == 1)if reshape:assert images.shape[3] == 1images = images.reshape(images.shape[0],images.shape[1] * images.shape[2])if dtype == dtypes.float32:# Convert from [0, 255] -> [0.0, 1.0].images = images.astype(numpy.float32)images = numpy.multiply(images, 1.0 / 255.0)self._images = imagesself._labels = labelsself._epochs_completed = 0self._index_in_epoch = 0@propertydef images(self):return self._images@propertydef labels(self):return self._labels@propertydef num_examples(self):return self._num_examples@propertydef epochs_completed(self):return self._epochs_completeddef next_batch(self, batch_size, fake_data=False, shuffle=True):"""Return the next `batch_size` examples from this data set."""if fake_data:fake_image = [1] * 784if self.one_hot:fake_label = [1] + [0] * 9else:fake_label = 0return [fake_image for _ in xrange(batch_size)], [fake_label for _ in xrange(batch_size)]start = self._index_in_epoch# Shuffle for the first epochif self._epochs_completed == 0 and start == 0 and shuffle:perm0 = numpy.arange(self._num_examples)numpy.random.shuffle(perm0)self._images = self.images[perm0]self._labels = self.labels[perm0]# Go to the next epochif start + batch_size > self._num_examples:# Finished epochself._epochs_completed += 1# Get the rest examples in this epochrest_num_examples = self._num_examples - startimages_rest_part = self._images[start:self._num_examples]labels_rest_part = self._labels[start:self._num_examples]# Shuffle the dataif shuffle:perm = numpy.arange(self._num_examples)numpy.random.shuffle(perm)self._images = self.images[perm]self._labels = self.labels[perm]# Start next epochstart = 0self._index_in_epoch = batch_size - rest_num_examplesend = self._index_in_epochimages_new_part = self._images[start:end]labels_new_part = self._labels[start:end]return numpy.concatenate((images_rest_part, images_new_part), axis=0) , numpy.concatenate((labels_rest_part, labels_new_part), axis=0)else:self._index_in_epoch += batch_sizeend = self._index_in_epochreturn self._images[start:end], self._labels[start:end]################################################################ #from here to operate the networkmnist = read_data_sets('MNIST_data', one_hot=True)def add_layer(inputs, in_size, out_size, activation_function=None):weights = tf.Variable(tf.random_normal([in_size, out_size]))biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,)wx_b = tf.matmul(inputs, weights) + biasesif activation_function is None:outputs = wx_belse:outputs = activation_function(wx_b,)return outputsxs = tf.placeholder(tf.float32, [None, 28*28]) ys = tf.placeholder(tf.float32, [None, 10])def compute_accuracy(v_xs, v_ys):global predictiony_pre = sess.run(prediction, feed_dict={xs: v_xs})correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))result = sess.run(accuracy, feed_dict={xs:v_xs, ys:v_ys})return resultprediction = add_layer(xs, 784, 10, activation_function = tf.nn.softmax)cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)sess = tf.Session()sess.run(tf.initialize_all_variables())for i in range(10000):batch_xs, batch_ys = mnist.train.next_batch(100)sess.run(train_step, feed_dict = {xs: batch_xs, ys: batch_ys})if i % 50 ==0:print "step:",i,", ",compute_accuracy(mnist.test.images, mnist.test.labels) File "<ipython-input-12-d56c6b11bc6b>", line 279print "step:",i,", ",compute_accuracy(mnist.test.images, mnist.test.labels)^ SyntaxError: invalid syntax # ex-10-ml.py# import tensorflow as tf import gzip import numpy import collections from tensorflow.python.framework import random_seed from tensorflow.python.framework import dtypes #from tensorflow.examples.tutorials.mnist import input_dataDatasets = collections.namedtuple('Datasets', ['train', 'validation', 'test' ])def _read32(bytestream):dt = numpy.dtype(numpy.uint32).newbyteorder('>')return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]def dense_to_one_hot(labels_dense, num_classes):"""Convert class labels from scalars to one-hot vectors."""num_labels = labels_dense.shape[0]index_offset = numpy.arange(num_labels) * num_classeslabels_one_hot = numpy.zeros((num_labels, num_classes))labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1return labels_one_hotdef extract_images(f):"""Extract the images into a 4D uint8 numpy array [index, y, x, depth].Args:f: A file object that can be passed into a gzip reader.Returns:data: A 4D uint8 numpy array [index, y, x, depth].Raises:ValueError: If the bytestream does not start with 2051."""print('Extracting', f.name)with gzip.GzipFile(fileobj=f) as bytestream:magic = _read32(bytestream)if magic != 2051:raise ValueError('Invalid magic number %d in MNIST image file: %s' %(magic, f.name))num_images = _read32(bytestream)rows = _read32(bytestream)cols = _read32(bytestream)buf = bytestream.read(rows * cols * num_images)data = numpy.frombuffer(buf, dtype=numpy.uint8)data = data.reshape(num_images, rows, cols, 1)return datadef extract_labels(f, one_hot=False, num_classes=10):"""Extract the labels into a 1D uint8 numpy array [index].Args:f: A file object that can be passed into a gzip reader.one_hot: Does one hot encoding for the result.num_classes: Number of classes for the one hot encoding.Returns:labels: a 1D uint8 numpy array.Raises:ValueError: If the bystream doesn't start with 2049."""print('Extracting', f.name)with gzip.GzipFile(fileobj=f) as bytestream:magic = _read32(bytestream)if magic != 2049:raise ValueError('Invalid magic number %d in MNIST label file: %s' %(magic, f.name))num_items = _read32(bytestream)buf = bytestream.read(num_items)labels = numpy.frombuffer(buf, dtype=numpy.uint8)if one_hot:return dense_to_one_hot(labels, num_classes)return labelsdef read_data_sets(train_dir,fake_data=False,one_hot=False,dtype=tf.float32,reshape=True,validation_size=5000,seed=None):'''if fake_data:def fake():return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype, seed=seed)train = fake()validation = fake()test = fake()return base.Datasets(train=train, validation=validation, test=test)'''TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'TEST_IMAGES = 't10k-images-idx3-ubyte.gz'TEST_LABELS = 't10k-labels-idx1-ubyte.gz'local_file = '/home/gao/dl_learning/py-example/MNIST_data/' + TRAIN_IMAGESwith open(local_file, 'rb') as f:train_images = extract_images(f)local_file = '/home/gao/dl_learning/py-example/MNIST_data/' + TRAIN_LABELSwith open(local_file, 'rb') as f:train_labels = extract_labels(f, one_hot=one_hot)local_file = '/home/gao/dl_learning/py-example/MNIST_data/' + TEST_IMAGESwith open(local_file, 'rb') as f:test_images = extract_images(f)local_file = '/home/gao/dl_learning/py-example/MNIST_data/' + TEST_LABELSwith open(local_file, 'rb') as f:test_labels = extract_labels(f, one_hot=one_hot)if not 0 <= validation_size <= len(train_images):raise ValueError('Validation size should be between 0 and {}. Received: {}.'.format(len(train_images), validation_size))validation_images = train_images[:validation_size]validation_labels = train_labels[:validation_size]train_images = train_images[validation_size:]train_labels = train_labels[validation_size:]options = dict(dtype=dtype, reshape=reshape, seed=seed)train = DataSet(train_images, train_labels, **options)validation = DataSet(validation_images, validation_labels, **options)test = DataSet(test_images, test_labels, **options)return Datasets(train=train, validation=validation, test=test)class DataSet(object):def __init__(self,images,labels,fake_data=False,one_hot=False,dtype=tf.float32,reshape=True,seed=None):"""Construct a DataSet.one_hot arg is used only if fake_data is true. `dtype` can be either`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into`[0, 1]`. Seed arg provides for convenient deterministic testing."""seed1, seed2 = random_seed.get_seed(seed)# If op level seed is not set, use whatever graph level seed is returnednumpy.random.seed(seed1 if seed is None else seed2)dtype = dtypes.as_dtype(dtype).base_dtypeif dtype not in (dtypes.uint8, dtypes.float32):raise TypeError('Invalid image dtype %r, expected uint8 or float32' %dtype)if fake_data:self._num_examples = 10000self.one_hot = one_hotelse:assert images.shape[0] == labels.shape[0], ('images.shape: %s labels.shape: %s' % (images.shape, labels.shape))self._num_examples = images.shape[0]# Convert shape from [num examples, rows, columns, depth]# to [num examples, rows*columns] (assuming depth == 1)if reshape:assert images.shape[3] == 1images = images.reshape(images.shape[0],images.shape[1] * images.shape[2])if dtype == dtypes.float32:# Convert from [0, 255] -> [0.0, 1.0].images = images.astype(numpy.float32)images = numpy.multiply(images, 1.0 / 255.0)self._images = imagesself._labels = labelsself._epochs_completed = 0self._index_in_epoch = 0@propertydef images(self):return self._images@propertydef labels(self):return self._labels@propertydef num_examples(self):return self._num_examples@propertydef epochs_completed(self):return self._epochs_completeddef next_batch(self, batch_size, fake_data=False, shuffle=True):"""Return the next `batch_size` examples from this data set."""if fake_data:fake_image = [1] * 784if self.one_hot:fake_label = [1] + [0] * 9else:fake_label = 0return [fake_image for _ in xrange(batch_size)], [fake_label for _ in xrange(batch_size)]start = self._index_in_epoch# Shuffle for the first epochif self._epochs_completed == 0 and start == 0 and shuffle:perm0 = numpy.arange(self._num_examples)numpy.random.shuffle(perm0)self._images = self.images[perm0]self._labels = self.labels[perm0]# Go to the next epochif start + batch_size > self._num_examples:# Finished epochself._epochs_completed += 1# Get the rest examples in this epochrest_num_examples = self._num_examples - startimages_rest_part = self._images[start:self._num_examples]labels_rest_part = self._labels[start:self._num_examples]# Shuffle the dataif shuffle:perm = numpy.arange(self._num_examples)numpy.random.shuffle(perm)self._images = self.images[perm]self._labels = self.labels[perm]# Start next epochstart = 0self._index_in_epoch = batch_size - rest_num_examplesend = self._index_in_epochimages_new_part = self._images[start:end]labels_new_part = self._labels[start:end]return numpy.concatenate((images_rest_part, images_new_part), axis=0) , numpy.concatenate((labels_rest_part, labels_new_part), axis=0)else:self._index_in_epoch += batch_sizeend = self._index_in_epochreturn self._images[start:end], self._labels[start:end]################################################################mnist = read_data_sets('MNIST_data', one_hot=True)def add_layer(inputs, in_size, out_size, activation_function=None):weights = tf.Variable(tf.random_normal([in_size, out_size]))#biases = tf.Variable(tf.zeros([1, out_size]) + 100)biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)wx_b = tf.matmul(inputs, weights) + biasesif activation_function is None:outputs = wx_belse:outputs = activation_function(wx_b,)return outputsxs = tf.placeholder(tf.float32, [None, 28*28]) ys = tf.placeholder(tf.float32, [None, 10])def compute_accuracy(v_xs, v_ys):global predictiony_pre = sess.run(prediction, feed_dict={xs: v_xs})correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))result = sess.run(accuracy, feed_dict={xs:v_xs, ys:v_ys})return result#layer1 = add_layer(xs, 784, 50, activation_function = tf.nn.tanh) #layer1 = add_layer(xs, 784, 30, activation_function = tf.nn.sigmoid) layer1 = add_layer(xs, 784, 50, activation_function = tf.nn.relu) layer2 = add_layer(layer1, 50, 50, activation_function = tf.nn.tanh) layer3 = add_layer(layer2, 50, 50, activation_function = tf.nn.tanh) layer4 = add_layer(layer3, 50, 50, activation_function = tf.nn.tanh)prediction = add_layer(layer4, 50, 10, activation_function = tf.nn.softmax)cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)sess = tf.Session()sess.run(tf.initialize_all_variables())for i in range(100000):batch_xs, batch_ys = mnist.train.next_batch(100)sess.run(layer1, feed_dict = {xs: batch_xs, ys: batch_ys})sess.run(train_step, feed_dict = {xs: batch_xs, ys:batch_ys})if i % 100 ==0:print "step:",i,", ",compute_accuracy(mnist.test.images, mnist.test.labels) File "<ipython-input-13-eaeb1867aa2d>", line 286print "step:",i,", ",compute_accuracy(mnist.test.images, mnist.test.labels)^ SyntaxError: invalid syntax # ex-11.py import tensorflow as tf from sklearn.datasets import load_digits from sklearn.cross_validation import train_test_split from sklearn.preprocessing import LabelBinarizerdigits = load_digits() x = digits.data y = digits.target #one-hot transform y = LabelBinarizer().fit_transform(y) x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3)def add_layer(inputs, in_size, out_size, layer_name, activation_function = None):weights = tf.Variable(tf.random_normal([in_size, out_size]))biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,)wx_b = tf.matmul(inputs, weights) + biasesif activation_function is None:outputs = wx_belse:outputs = activation_function(wx_b)tf.summary.histogram(layer_name + '/outputs', outputs)return outputsxs = tf.placeholder(tf.float32, [None, 8*8]) ys = tf.placeholder(tf.float32, [None, 10])layer1 = add_layer(xs, 64, 100, 'layer1', activation_function = tf.nn.tanh) prediction = add_layer(layer1, 100, 10, 'layer2', activation_function = tf.nn.softmax)cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))tf.summary.scalar('loss', cross_entropy)train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)sess = tf.Session() merged = tf.summary.merge_all()train_writer = tf.train.SummaryWriter("logs/train", sess.graph) test_writer = tf.train.SummaryWriter("logs/test", sess.graph)sess.run(tf.initialize_all_variables())for i in range(1000):sess.run(train_step, feed_dict = {xs: x_train, ys:y_train})if i % 100==0:train_result = sess.run(merged, feed_dict={xs:x_train, ys:y_train})test_result = sess.run(merged, feed_dict={xs:x_test, ys:y_test})train_writer.add_summary(train_result, i)test_writer.add_summary(test_result, i) C:\ProgramData\Anaconda3\lib\site-packages\sklearn\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20."This module will be removed in 0.20.", DeprecationWarning)---------------------------------------------------------------------------AttributeError Traceback (most recent call last)<ipython-input-14-212ae64f528b> in <module>()39 merged = tf.summary.merge_all()40 ---> 41 train_writer = tf.train.SummaryWriter("logs/train", sess.graph)42 test_writer = tf.train.SummaryWriter("logs/test", sess.graph)43 AttributeError: module 'tensorflow.python.training.training' has no attribute 'SummaryWriter' # ex-11-dropout.py import tensorflow as tf from sklearn.datasets import load_digits from sklearn.cross_validation import train_test_split from sklearn.preprocessing import LabelBinarizerdigits = load_digits() x = digits.data y = digits.target #one-hot transform y = LabelBinarizer().fit_transform(y) x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3)def add_layer(inputs, in_size, out_size, layer_name, activation_function = None):weights = tf.Variable(tf.random_normal([in_size, out_size]))biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,)wx_b = tf.matmul(inputs, weights) + biases#drop out and keep a proportionwx_b = tf.nn.dropout(wx_b, keep_prob)if activation_function is None:outputs = wx_belse:outputs = activation_function(wx_b)tf.histogram_summary(layer_name + '/outputs', outputs)return outputskeep_prob = tf.placeholder(tf.float32) xs = tf.placeholder(tf.float32, [None, 8*8]) ys = tf.placeholder(tf.float32, [None, 10])layer1 = add_layer(xs, 64, 100, 'layer1', activation_function = tf.nn.tanh) prediction = add_layer(layer1, 100, 10, 'layer2', activation_function = tf.nn.softmax)cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))tf.scalar_summary('loss', cross_entropy)train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)sess = tf.Session() merged = tf.merge_all_summaries()train_writer = tf.train.SummaryWriter("logs/train", sess.graph) test_writer = tf.train.SummaryWriter("logs/test", sess.graph)sess.run(tf.initialize_all_variables())for i in range(1000):sess.run(train_step, feed_dict = {xs: x_train, ys:y_train, keep_prob:0.7})if i % 100==0:train_result = sess.run(merged, feed_dict={xs:x_train, ys:y_train, keep_prob: 0.7})test_result = sess.run(merged, feed_dict={xs:x_test, ys:y_test, keep_prob: 0.7})train_writer.add_summary(train_result, i)test_writer.add_summary(test_result, i) ---------------------------------------------------------------------------AttributeError Traceback (most recent call last)<ipython-input-15-482298ce8d49> in <module>()30 ys = tf.placeholder(tf.float32, [None, 10])31 ---> 32 layer1 = add_layer(xs, 64, 100, 'layer1', activation_function = tf.nn.tanh)33 prediction = add_layer(layer1, 100, 10, 'layer2', activation_function = tf.nn.softmax)34 <ipython-input-15-482298ce8d49> in add_layer(inputs, in_size, out_size, layer_name, activation_function)23 else:24 outputs = activation_function(wx_b) ---> 25 tf.histogram_summary(layer_name + '/outputs', outputs)26 return outputs27 AttributeError: module 'tensorflow' has no attribute 'histogram_summary'input_data.py
# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # =============================================================================="""Functions for downloading and reading MNIST data.""" from __future__ import absolute_import from __future__ import division from __future__ import print_functionimport gzip import os import tempfileimport numpy from six.moves import urllib from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_setsmnist.py
# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # =============================================================================="""Functions for downloading and reading MNIST data."""from __future__ import absolute_import from __future__ import division from __future__ import print_functionimport gzipimport numpy from six.moves import xrange # pylint: disable=redefined-builtinfrom tensorflow.contrib.learn.python.learn.datasets import base from tensorflow.python.framework import dtypes from tensorflow.python.framework import random_seed# CVDF mirror of http://yann.lecun.com/exdb/mnist/ SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'def _read32(bytestream):dt = numpy.dtype(numpy.uint32).newbyteorder('>')return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]def extract_images(f):"""Extract the images into a 4D uint8 numpy array [index, y, x, depth].Args:f: A file object that can be passed into a gzip reader.Returns:data: A 4D uint8 numpy array [index, y, x, depth].Raises:ValueError: If the bytestream does not start with 2051."""print('Extracting', f.name)with gzip.GzipFile(fileobj=f) as bytestream:magic = _read32(bytestream)if magic != 2051:raise ValueError('Invalid magic number %d in MNIST image file: %s' %(magic, f.name))num_images = _read32(bytestream)rows = _read32(bytestream)cols = _read32(bytestream)buf = bytestream.read(rows * cols * num_images)data = numpy.frombuffer(buf, dtype=numpy.uint8)data = data.reshape(num_images, rows, cols, 1)return datadef dense_to_one_hot(labels_dense, num_classes):"""Convert class labels from scalars to one-hot vectors."""num_labels = labels_dense.shape[0]index_offset = numpy.arange(num_labels) * num_classeslabels_one_hot = numpy.zeros((num_labels, num_classes))labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1return labels_one_hotdef extract_labels(f, one_hot=False, num_classes=10):"""Extract the labels into a 1D uint8 numpy array [index].Args:f: A file object that can be passed into a gzip reader.one_hot: Does one hot encoding for the result.num_classes: Number of classes for the one hot encoding.Returns:labels: a 1D uint8 numpy array.Raises:ValueError: If the bystream doesn't start with 2049."""print('Extracting', f.name)with gzip.GzipFile(fileobj=f) as bytestream:magic = _read32(bytestream)if magic != 2049:raise ValueError('Invalid magic number %d in MNIST label file: %s' %(magic, f.name))num_items = _read32(bytestream)buf = bytestream.read(num_items)labels = numpy.frombuffer(buf, dtype=numpy.uint8)if one_hot:return dense_to_one_hot(labels, num_classes)return labelsclass DataSet(object):def __init__(self,images,labels,fake_data=False,one_hot=False,dtype=dtypes.float32,reshape=True,seed=None):"""Construct a DataSet.one_hot arg is used only if fake_data is true. `dtype` can be either`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into`[0, 1]`. Seed arg provides for convenient deterministic testing."""seed1, seed2 = random_seed.get_seed(seed)# If op level seed is not set, use whatever graph level seed is returnednumpy.random.seed(seed1 if seed is None else seed2)dtype = dtypes.as_dtype(dtype).base_dtypeif dtype not in (dtypes.uint8, dtypes.float32):raise TypeError('Invalid image dtype %r, expected uint8 or float32' %dtype)if fake_data:self._num_examples = 10000self.one_hot = one_hotelse:assert images.shape[0] == labels.shape[0], ('images.shape: %s labels.shape: %s' % (images.shape, labels.shape))self._num_examples = images.shape[0]# Convert shape from [num examples, rows, columns, depth]# to [num examples, rows*columns] (assuming depth == 1)if reshape:assert images.shape[3] == 1images = images.reshape(images.shape[0],images.shape[1] * images.shape[2])if dtype == dtypes.float32:# Convert from [0, 255] -> [0.0, 1.0].images = images.astype(numpy.float32)images = numpy.multiply(images, 1.0 / 255.0)self._images = imagesself._labels = labelsself._epochs_completed = 0self._index_in_epoch = 0@propertydef images(self):return self._images@propertydef labels(self):return self._labels@propertydef num_examples(self):return self._num_examples@propertydef epochs_completed(self):return self._epochs_completeddef next_batch(self, batch_size, fake_data=False, shuffle=True):"""Return the next `batch_size` examples from this data set."""if fake_data:fake_image = [1] * 784if self.one_hot:fake_label = [1] + [0] * 9else:fake_label = 0return [fake_image for _ in xrange(batch_size)], [fake_label for _ in xrange(batch_size)]start = self._index_in_epoch# Shuffle for the first epochif self._epochs_completed == 0 and start == 0 and shuffle:perm0 = numpy.arange(self._num_examples)numpy.random.shuffle(perm0)self._images = self.images[perm0]self._labels = self.labels[perm0]# Go to the next epochif start + batch_size > self._num_examples:# Finished epochself._epochs_completed += 1# Get the rest examples in this epochrest_num_examples = self._num_examples - startimages_rest_part = self._images[start:self._num_examples]labels_rest_part = self._labels[start:self._num_examples]# Shuffle the dataif shuffle:perm = numpy.arange(self._num_examples)numpy.random.shuffle(perm)self._images = self.images[perm]self._labels = self.labels[perm]# Start next epochstart = 0self._index_in_epoch = batch_size - rest_num_examplesend = self._index_in_epochimages_new_part = self._images[start:end]labels_new_part = self._labels[start:end]return numpy.concatenate((images_rest_part, images_new_part), axis=0) , numpy.concatenate((labels_rest_part, labels_new_part), axis=0)else:self._index_in_epoch += batch_sizeend = self._index_in_epochreturn self._images[start:end], self._labels[start:end]def read_data_sets(train_dir,fake_data=False,one_hot=False,dtype=dtypes.float32,reshape=True,validation_size=5000,seed=None):if fake_data:def fake():return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype, seed=seed)train = fake()validation = fake()test = fake()return base.Datasets(train=train, validation=validation, test=test)TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'TEST_IMAGES = 't10k-images-idx3-ubyte.gz'TEST_LABELS = 't10k-labels-idx1-ubyte.gz'local_file = base.maybe_download(TRAIN_IMAGES, train_dir,SOURCE_URL + TRAIN_IMAGES)with open(local_file, 'rb') as f:train_images = extract_images(f)local_file = base.maybe_download(TRAIN_LABELS, train_dir,SOURCE_URL + TRAIN_LABELS)with open(local_file, 'rb') as f:train_labels = extract_labels(f, one_hot=one_hot)local_file = base.maybe_download(TEST_IMAGES, train_dir,SOURCE_URL + TEST_IMAGES)with open(local_file, 'rb') as f:test_images = extract_images(f)local_file = base.maybe_download(TEST_LABELS, train_dir,SOURCE_URL + TEST_LABELS)with open(local_file, 'rb') as f:test_labels = extract_labels(f, one_hot=one_hot)if not 0 <= validation_size <= len(train_images):raise ValueError('Validation size should be between 0 and {}. Received: {}.'.format(len(train_images), validation_size))validation_images = train_images[:validation_size]validation_labels = train_labels[:validation_size]train_images = train_images[validation_size:]train_labels = train_labels[validation_size:]options = dict(dtype=dtype, reshape=reshape, seed=seed)train = DataSet(train_images, train_labels, **options)validation = DataSet(validation_images, validation_labels, **options)test = DataSet(test_images, test_labels, **options)return base.Datasets(train=train, validation=validation, test=test)def load_mnist(train_dir='MNIST-data'):return read_data_sets(train_dir)轉載于:https://www.cnblogs.com/q735613050/p/7745962.html
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