如何保存pb文件,并调用测试
2017-07-29 11:42
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读取数据集函数:datasets_mnist.py
训练函数:mnist.py
测试函数: test.py
from __future__ import absolute_import from __future__ import division from __future__ import print_function import gzip import os import numpy from six.moves import xrange # pylint: disable=redefined-builtin from 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 data 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_classes labels_one_hot = numpy.zeros((num_labels, num_classes)) labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 return labels_one_hot # def 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 labels # class 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 returned numpy.random.seed(seed1 if seed is None else seed2) dtype = dtypes.as_dtype(dtype).base_dtype if dtype not in (dtypes.uint8, dtypes.float32): raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype) if fake_data: self._num_examples = 10000 self.one_hot = one_hot else: 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] == 1 images = 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 = images self._labels = labels self._epochs_completed = 0 self._index_in_epoch = 0 @property def images(self): return self._images @property def labels(self): return self._labels @property def num_examples(self): return self._num_examples @property def epochs_completed(self): return self._epochs_completed def 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] * 784 if self.one_hot: fake_label = [1] + [0] * 9 else: fake_label = 0 return [fake_image for _ in xrange(batch_size)], [ fake_label for _ in xrange(batch_size) ] start = self._index_in_epoch # Shuffle for the first epoch if 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 epoch if start + batch_size > self._num_examples: # Finished epoch self._epochs_completed += 1 # Get the rest examples in this epoch rest_num_examples = self._num_examples - start images_rest_part = self._images[start:self._num_examples] labels_rest_part = self._labels[start:self._num_examples] # Shuffle the data if shuffle: perm = numpy.arange(self._num_examples) numpy.random.shuffle(perm) self._images = self.images[perm] self._labels = self.labels[perm] # Start next epoch start = 0 self._index_in_epoch = batch_size - rest_num_examples end = self._index_in_epoch images_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_size end = self._index_in_epoch return 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 = os.path.join('datasets','train-images-idx3-ubyte.gz') with open(local_file, 'rb') as f: train_images = extract_images(f) local_file = os.path.join('datasets','train-labels-idx1-ubyte.gz') with open(local_file, 'rb') as f: train_labels = extract_labels(f, one_hot=one_hot) local_file = os.path.join('datasets','t10k-images-idx3-ubyte.gz') with open(local_file, 'rb') as f: test_images = extract_images(f) local_file =os.path.join('datasets','t10k-labels-idx1-ubyte.gz') 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:] train = DataSet( train_images, train_labels, dtype=dtype, reshape=reshape, seed=seed) validation = DataSet( validation_images, validation_labels, dtype=dtype, reshape=reshape, seed=seed) test = DataSet( test_images, test_labels, dtype=dtype, reshape=reshape, seed=seed) return train,validation,test
训练函数:mnist.py
from __future__ import absolute_import, unicode_literals from datasets_mnist import read_data_sets import tensorflow as tf import shutil import os.path export_dir = 'log/' if os.path.exists(export_dir): shutil.rmtree(export_dir) def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.consta c60b nt(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') train,validation,test =read_data_sets("datasets/", one_hot=True) g = tf.Graph() with g.as_default(): x = tf.placeholder("float", shape=[None, 784]) y_ = tf.placeholder("float", shape=[None, 10]) W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1, 28, 28, 1]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) sess = tf.Session() sess.run(tf.initialize_all_variables()) for i in range(2001): batch = train.next_batch(100) if i % 100 == 0: train_accuracy = accuracy.eval( {x: batch[0], y_: batch[1], keep_prob: 1.0}, sess) print ("step %d, training accuracy %g" % (i, train_accuracy)) train_step.run( {x: batch[0], y_: batch[1], keep_prob: 0.5}, sess) print( "test accuracy %g" % accuracy.eval( {x: test.images, y_: test.labels, keep_prob: 1.0}, sess)) # Store variable _W_conv1 = W_conv1.eval(sess) _b_conv1 = b_conv1.eval(sess) _W_conv2 = W_conv2.eval(sess) _b_conv2 = b_conv2.eval(sess) _W_fc1 = W_fc1.eval(sess) _b_fc1 = b_fc1.eval(sess) _W_fc2 = W_fc2.eval(sess) _b_fc2 = b_fc2.eval(sess) sess.close() # Create new graph for exporting g_2 = tf.Graph() with g_2.as_default(): x_2 = tf.placeholder("float", shape=[None, 784], name="inputdata") W_conv1_2 = tf.constant(_W_conv1, name="constant_W_conv1") b_conv1_2 = tf.constant(_b_conv1, name="constant_b_conv1") x_image_2 = tf.reshape(x_2, [-1, 28, 28, 1]) h_conv1_2 = tf.nn.relu(conv2d(x_image_2, W_conv1_2) + b_conv1_2) h_pool1_2 = max_pool_2x2(h_conv1_2) W_conv2_2 = tf.constant(_W_conv2, name="constant_W_conv2") b_conv2_2 = tf.constant(_b_conv2, name="constant_b_conv2") h_conv2_2 = tf.nn.relu(conv2d(h_pool1_2, W_conv2_2) + b_conv2_2) h_pool2_2 = max_pool_2x2(h_conv2_2) W_fc1_2 = tf.constant(_W_fc1, name="constant_W_fc1") b_fc1_2 = tf.constant(_b_fc1, name="constanexport_dirt_b_fc1") h_pool2_flat_2 = tf.reshape(h_pool2_2, [-1, 7 * 7 * 64]) h_fc1_2 = tf.nn.relu(tf.matmul(h_pool2_flat_2, W_fc1_2) + b_fc1_2) W_fc2_2 = tf.constant(_W_fc2, name="constant_W_fc2") b_fc2_2 = tf.constant(_b_fc2, name="constant_b_fc2") # DropOut is skipped for exported graph. y_conv_2 = tf.nn.softmax(tf.matmul(h_fc1_2, W_fc2_2) + b_fc2_2, name="outputdata") sess_2 = tf.Session() init_2 = tf.initialize_all_variables(); sess_2.run(init_2) graph_def = g_2.as_graph_def() tf.train.write_graph(graph_def, export_dir, 'expert-graph.weights', as_text=False) # Test trained model y__2 = tf.placeholder("float", [None, 10]) correct_prediction_2 = tf.equal(tf.argmax(y_conv_2, 1), tf.argmax(y__2, 1)) accuracy_2 = tf.reduce_mean(tf.cast(correct_prediction_2, "float")) print( "check accuracy %g" % accuracy_2.eval( {x_2: test.images, y__2: test.labels}, sess_2))
测试函数: test.py
from __future__ import absolute_import, unicode_literals from datasets_mnist import read_data_sets import tensorflow as tf train,validation,test = read_data_sets("datasets/", one_hot=True) with tf.Graph().as_default(): output_graph_def = tf.GraphDef() output_graph_path = 'log/expert-graph.weights' # sess.graph.add_to_collection("input", mnist.test.images) with open(output_graph_path, "rb") as f: output_graph_def.ParseFromString(f.read()) tf.import_graph_def(output_graph_def, name="") with tf.Session() as sess: tf.initialize_all_variables().run() input_x = sess.graph.get_tensor_by_name("inputdata:0") print( input_x) output = sess.graph.get_tensor_by_name("outputdata:0") print( output) y_conv_2 = sess.run(output,{input_x:test.images}) print( "y_conv_2", y_conv_2) # Test trained model #y__2 = tf.placeholder("float", [None, 10]) y__2 = test.labels correct_prediction_2 = tf.equal(tf.argmax(y_conv_2, 1), tf.argmax(y__2, 1)) print ("correct_prediction_2", correct_prediction_2 ) accuracy_2 = tf.reduce_mean(tf.cast(correct_prediction_2, "float")) print ("accuracy_2", accuracy_2) print ("check accuracy %g" % accuracy_2.eval())
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