Tensorflow
教程
指数滑动平均(ExponentialMovingAverage)EMA：
- https://blog.csdn.net/qq_14845119/article/details/78767544
- https://blog.csdn.net/u012436149/article/details/56484572
- https://www.cnblogs.com/cloud-ken/p/7521609.html
截断正态分布（Truncated normal distribution）：https://blog.csdn.net/lanchunhui/article/details/61623189
W3Cschool：https://www.w3cschool.cn/tensorflow_python/
tensorflow入门：https://blog.csdn.net/lengguoxing/article/details/78456279
tensorflow快餐教程：https://yq.aliyun.com/articles/582122?spm=a2c4e.11153940.blogcont582490.20.28f86afavnqV2j
Tensorflow游乐场:http://playground.tensorflow.org
tensorflow安装
1、python3环境安装
2、安装tensorflow
- pip3 install tensorflow
- pip3 install tensorflow-gpu
GPU：
显卡配置
安装CUDA ：https://docs.nvidia.com/cuda/cuda-installation-guidelinux/
安装cnDNN SDK： https://docs.nvidia.com/deeplearning/sdk/cudnninstall/
查看显卡计算能力：https://developer.nvidia.com/cuda-gpus
概念
（1）iteration：表示1次迭代（也叫training step），每次迭代更新1次网络结构的参数；
（2）batch-size：1次迭代所使用的样本量；
（3）epoch：1个epoch表示过了1遍训练集中的所有样本。
值得注意的是，在深度学习领域中，常用带mini-batch的随机梯度下降算法（Stochastic Gradient Descent, SGD）训练深层结构，它有一个好处就是并不需要遍历全部的样本，当数据量非常大时十分有效。此时，可根据实际问题来定义epoch，例如定义10000次迭代为1个epoch，若每次迭代的batch-size设为256，那么1个epoch相当于过了2560000个训练样本
数字识别
安装库
matplotlib==3.0.0 mnist==0.2.2 numpy==1.14.5 pandas==0.23.4 Pillow==5.2.0 python-mnist==0.6 scikit-learn==0.19.2 scipy==1.1.0 tensorflow==1.10.0
版本一（CNN卷积神经网络）
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 input_layer = tf.reshape(features["x"], [-1, 28, 28, 1]) # Convolutional Layer #1 conv1 = tf.layers.conv2d( inputs=input_layer, filters=32, kernel_size=[5, 5], padding="same", activation=tf.nn.relu) # Pooling Layer #1 pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) # Convolutional Layer #2 and Pooling Layer #2 conv2 = tf.layers.conv2d( inputs=pool1, filters=64, kernel_size=[5, 5], padding="same", activation=tf.nn.relu) pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) # Dense Layer pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64]) dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu) dropout = tf.layers.dropout(inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN) # Logits Layer 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(): mnist = tf.contrib.learn.datasets.load_dataset("mnist") train_data = mnist.train.images train_labels = np.asarray(mnist.train.labels, dtype=np.int32) eval_data = mnist.test.images eval_labels = np.asarray(mnist.test.labels, dtype=np.int32) mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model") # Set up logging for predictions 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=200,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()
版本二
# Copyright 2017 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. """Convolutional Neural Network Estimator for MNIST, built with tf.layers.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl import app as absl_app from absl import flags import tensorflow as tf # pylint: disable=g-bad-import-order from official.mnist import dataset from official.utils.flags import core as flags_core from official.utils.logs import hooks_helper from official.utils.misc import distribution_utils from official.utils.misc import model_helpers LEARNING_RATE = 1e-4 def create_model(data_format): """Model to recognize digits in the MNIST dataset. Network structure is equivalent to: https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/examples/tutorials/mnist/mnist_deep.py and https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py But uses the tf.keras API. Args: data_format: Either 'channels_first' or 'channels_last'. 'channels_first' is typically faster on GPUs while 'channels_last' is typically faster on CPUs. See https://www.tensorflow.org/performance/performance_guide#data_formats Returns: A tf.keras.Model. """ if data_format == 'channels_first': input_shape = [1, 28, 28] else: assert data_format == 'channels_last' input_shape = [28, 28, 1] l = tf.keras.layers max_pool = l.MaxPooling2D( (2, 2), (2, 2), padding='same', data_format=data_format) # The model consists of a sequential chain of layers, so tf.keras.Sequential # (a subclass of tf.keras.Model) makes for a compact description. return tf.keras.Sequential( [ l.Reshape( target_shape=input_shape, input_shape=(28 * 28,)), l.Conv2D( 32, 5, padding='same', data_format=data_format, activation=tf.nn.relu), max_pool, l.Conv2D( 64, 5, padding='same', data_format=data_format, activation=tf.nn.relu), max_pool, l.Flatten(), l.Dense(1024, activation=tf.nn.relu), l.Dropout(0.4), l.Dense(10) ]) def define_mnist_flags(): flags_core.define_base() flags_core.define_performance(num_parallel_calls=False) flags_core.define_image() flags.adopt_module_key_flags(flags_core) flags_core.set_defaults(data_dir='/tmp/mnist_data', model_dir='/tmp/mnist_model', batch_size=100, train_epochs=40) def model_fn(features, labels, mode, params): """The model_fn argument for creating an Estimator.""" model = create_model(params['data_format']) image = features if isinstance(image, dict): image = features['image'] if mode == tf.estimator.ModeKeys.PREDICT: logits = model(image, training=False) predictions = { 'classes': tf.argmax(logits, axis=1), 'probabilities': tf.nn.softmax(logits), } return tf.estimator.EstimatorSpec( mode=tf.estimator.ModeKeys.PREDICT, predictions=predictions, export_outputs={ 'classify': tf.estimator.export.PredictOutput(predictions) }) if mode == tf.estimator.ModeKeys.TRAIN: optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE) logits = model(image, training=True) loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) accuracy = tf.metrics.accuracy( labels=labels, predictions=tf.argmax(logits, axis=1)) # Name tensors to be logged with LoggingTensorHook. tf.identity(LEARNING_RATE, 'learning_rate') tf.identity(loss, 'cross_entropy') tf.identity(accuracy[1], name='train_accuracy') # Save accuracy scalar to Tensorboard output. tf.summary.scalar('train_accuracy', accuracy[1]) return tf.estimator.EstimatorSpec( mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=optimizer.minimize(loss, tf.train.get_or_create_global_step())) if mode == tf.estimator.ModeKeys.EVAL: logits = model(image, training=False) loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) return tf.estimator.EstimatorSpec( mode=tf.estimator.ModeKeys.EVAL, loss=loss, eval_metric_ops={ 'accuracy': tf.metrics.accuracy( labels=labels, predictions=tf.argmax(logits, axis=1)), }) def run_mnist(flags_obj): """Run MNIST training and eval loop. Args: flags_obj: An object containing parsed flag values. """ model_helpers.apply_clean(flags_obj) model_function = model_fn session_config = tf.ConfigProto( inter_op_parallelism_threads=flags_obj.inter_op_parallelism_threads, intra_op_parallelism_threads=flags_obj.intra_op_parallelism_threads, allow_soft_placement=True) distribution_strategy = distribution_utils.get_distribution_strategy( flags_core.get_num_gpus(flags_obj), flags_obj.all_reduce_alg) run_config = tf.estimator.RunConfig( train_distribute=distribution_strategy, session_config=session_config) data_format = flags_obj.data_format if data_format is None: data_format = ('channels_first' if tf.test.is_built_with_cuda() else 'channels_last') mnist_classifier = tf.estimator.Estimator( model_fn=model_function, model_dir=flags_obj.model_dir, config=run_config, params={ 'data_format': data_format, }) # Set up training and evaluation input functions. def train_input_fn(): """Prepare data for training.""" # When choosing shuffle buffer sizes, larger sizes result in better # randomness, while smaller sizes use less memory. MNIST is a small # enough dataset that we can easily shuffle the full epoch. ds = dataset.train(flags_obj.data_dir) ds = ds.cache().shuffle(buffer_size=50000).batch(flags_obj.batch_size) # Iterate through the dataset a set number (`epochs_between_evals`) of times # during each training session. ds = ds.repeat(flags_obj.epochs_between_evals) return ds def eval_input_fn(): return dataset.test(flags_obj.data_dir).batch( flags_obj.batch_size).make_one_shot_iterator().get_next() # Set up hook that outputs training logs every 100 steps. train_hooks = hooks_helper.get_train_hooks( flags_obj.hooks, model_dir=flags_obj.model_dir, batch_size=flags_obj.batch_size) # Train and evaluate model. for _ in range(flags_obj.train_epochs // flags_obj.epochs_between_evals): mnist_classifier.train(input_fn=train_input_fn, hooks=train_hooks) eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn) print('\nEvaluation results:\n\t%s\n' % eval_results) if model_helpers.past_stop_threshold(flags_obj.stop_threshold,eval_results['accuracy']): break # Export the model if flags_obj.export_dir is not None: image = tf.placeholder(tf.float32, [None, 28, 28]) input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({ 'image': image, }) mnist_classifier.export_savedmodel(flags_obj.export_dir, input_fn) def main(_): run_mnist(flags.FLAGS) if __name__ == '__main__': tf.logging.set_verbosity(tf.logging.INFO) define_mnist_flags() absl_app.run(main)
版本三
from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import gzip import os import sys import time import numpy from six.moves import urllib from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf # CVDF mirror of http://yann.lecun.com/exdb/mnist/ SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/' WORK_DIRECTORY = 'data' IMAGE_SIZE = 28 NUM_CHANNELS = 1 PIXEL_DEPTH = 255 NUM_LABELS = 10 VALIDATION_SIZE = 5000 # Size of the validation set. SEED = 66478 # Set to None for random seed. BATCH_SIZE = 64 NUM_EPOCHS = 10 EVAL_BATCH_SIZE = 64 EVAL_FREQUENCY = 100 # Number of steps between evaluations. FLAGS = None def data_type(): """Return the type of the activations, weights, and placeholder variables.""" if FLAGS.use_fp16: return tf.float16 else: return tf.float32 def maybe_download(filename): """Download the data from Yann's website, unless it's already here.""" if not tf.gfile.Exists(WORK_DIRECTORY): tf.gfile.MakeDirs(WORK_DIRECTORY) filepath = os.path.join(WORK_DIRECTORY, filename) if not tf.gfile.Exists(filepath): filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath) with tf.gfile.GFile(filepath) as f: size = f.size() print('Successfully downloaded', filename, size, 'bytes.') return filepath def extract_data(filename, num_images): """Extract the images into a 4D tensor [image index, y, x, channels]. Values are rescaled from [0, 255] down to [-0.5, 0.5]. """ print('Extracting', filename) with gzip.open(filename) as bytestream: bytestream.read(16) buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS) data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32) data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS) return data def extract_labels(filename, num_images): """Extract the labels into a vector of int64 label IDs.""" print('Extracting', filename) with gzip.open(filename) as bytestream: bytestream.read(8) buf = bytestream.read(1 * num_images) labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64) return labels def fake_data(num_images): """Generate a fake dataset that matches the dimensions of MNIST.""" data = numpy.ndarray( shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS), dtype=numpy.float32) labels = numpy.zeros(shape=(num_images,), dtype=numpy.int64) for image in xrange(num_images): label = image % 2 data[image, :, :, 0] = label - 0.5 labels[image] = label return data, labels def error_rate(predictions, labels): """Return the error rate based on dense predictions and sparse labels.""" return 100.0 - ( 100.0 * numpy.sum(numpy.argmax(predictions, 1) == labels) / predictions.shape[0]) def main(_): if FLAGS.self_test: print('Running self-test.') train_data, train_labels = fake_data(256) validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE) test_data, test_labels = fake_data(EVAL_BATCH_SIZE) num_epochs = 1 else: # Get the data. train_data_filename = maybe_download('train-images-idx3-ubyte.gz') train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz') test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz') test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz') # Extract it into numpy arrays. train_data = extract_data(train_data_filename, 60000) train_labels = extract_labels(train_labels_filename, 60000) test_data = extract_data(test_data_filename, 10000) test_labels = extract_labels(test_labels_filename, 10000) # Generate a validation set. validation_data = train_data[:VALIDATION_SIZE, ...] validation_labels = train_labels[:VALIDATION_SIZE] train_data = train_data[VALIDATION_SIZE:, ...] train_labels = train_labels[VALIDATION_SIZE:] num_epochs = NUM_EPOCHS train_size = train_labels.shape[0] # This is where training samples and labels are fed to the graph. # These placeholder nodes will be fed a batch of training data at each # training step using the {feed_dict} argument to the Run() call below. train_data_node = tf.placeholder( data_type(), shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)) train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,)) eval_data = tf.placeholder( data_type(), shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)) # The variables below hold all the trainable weights. They are passed an # initial value which will be assigned when we call: # {tf.global_variables_initializer().run()} conv1_weights = tf.Variable(tf.truncated_normal( [5, 5, NUM_CHANNELS, 32], # 5x5 filter, depth 32. stddev=0.1, seed=SEED, dtype=data_type() )) conv1_biases = tf.Variable(tf.zeros([32], dtype=data_type())) conv2_weights = tf.Variable(tf.truncated_normal( [5, 5, 32, 64], stddev=0.1, seed=SEED, dtype=data_type())) conv2_biases = tf.Variable(tf.constant(0.1, shape=[64], dtype=data_type())) fc1_weights = tf.Variable( # fully connected, depth 512. tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512], stddev=0.1, seed=SEED, dtype=data_type())) fc1_biases = tf.Variable(tf.constant(0.1, shape=[512], dtype=data_type())) fc2_weights = tf.Variable(tf.truncated_normal([512, NUM_LABELS], stddev=0.1, seed=SEED, dtype=data_type())) fc2_biases = tf.Variable(tf.constant( 0.1, shape=[NUM_LABELS], dtype=data_type())) # We will replicate the model structure for the training subgraph, as well # as the evaluation subgraphs, while sharing the trainable parameters. def model(data, train=False): """The Model definition.""" # 2D convolution, with 'SAME' padding (i.e. the output feature map has # the same size as the input). Note that {strides} is a 4D array whose # shape matches the data layout: [image index, y, x, depth]. conv = tf.nn.conv2d(data, conv1_weights, strides=[1, 1, 1, 1], padding='SAME') # Bias and rectified linear non-linearity. relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases)) # Max pooling. The kernel size spec {ksize} also follows the layout of # the data. Here we have a pooling window of 2, and a stride of 2. pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') conv = tf.nn.conv2d(pool, conv2_weights, strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases)) pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # Reshape the feature map cuboid into a 2D matrix to feed it to the # fully connected layers. pool_shape = pool.get_shape().as_list() reshape = tf.reshape( pool, [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]]) # Fully connected layer. Note that the '+' operation automatically # broadcasts the biases. hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases) # Add a 50% dropout during training only. Dropout also scales # activations such that no rescaling is needed at evaluation time. if train: hidden = tf.nn.dropout(hidden, 0.5, seed=SEED) return tf.matmul(hidden, fc2_weights) + fc2_biases # Training computation: logits + cross-entropy loss. logits = model(train_data_node, True) loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( labels=train_labels_node, logits=logits)) # L2 regularization for the fully connected parameters. regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) + tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases)) # Add the regularization term to the loss. loss += 5e-4 * regularizers # Optimizer: set up a variable that's incremented once per batch and # controls the learning rate decay. batch = tf.Variable(0, dtype=data_type()) # Decay once per epoch, using an exponential schedule starting at 0.01. learning_rate = tf.train.exponential_decay( 0.01, # Base learning rate. batch * BATCH_SIZE, # Current index into the dataset. train_size, # Decay step. 0.95, # Decay rate. staircase=True) # Use simple momentum for the optimization. 0.9冲量因子 optimizer = tf.train.MomentumOptimizer(learning_rate,0.9).minimize(loss,global_step=batch) # Predictions for the current training minibatch. train_prediction = tf.nn.softmax(logits) # Predictions for the test and validation, which we'll compute less often. eval_prediction = tf.nn.softmax(model(eval_data)) # Small utility function to evaluate a dataset by feeding batches of data to # {eval_data} and pulling the results from {eval_predictions}. # Saves memory and enables this to run on smaller GPUs. def eval_in_batches(data, sess): """Get all predictions for a dataset by running it in small batches.""" size = data.shape[0] if size < EVAL_BATCH_SIZE: raise ValueError("batch size for evals larger than dataset: %d" % size) predictions = numpy.ndarray(shape=(size, NUM_LABELS), dtype=numpy.float32) for begin in xrange(0, size, EVAL_BATCH_SIZE): end = begin + EVAL_BATCH_SIZE if end <= size: predictions[begin:end, :] = sess.run( eval_prediction, feed_dict={eval_data: data[begin:end, ...]}) else: batch_predictions = sess.run( eval_prediction, feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]}) predictions[begin:, :] = batch_predictions[begin - size:, :] return predictions # Create a local session to run the training. start_time = time.time() with tf.Session() as sess: # Run all the initializers to prepare the trainable parameters. tf.global_variables_initializer().run() print('Initialized!') # Loop through training steps. for step in xrange(int(num_epochs * train_size) // BATCH_SIZE): # Compute the offset of the current minibatch in the data. # Note that we could use better randomization across epochs. offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE) batch_data = train_data[offset:(offset + BATCH_SIZE), ...] batch_labels = train_labels[offset:(offset + BATCH_SIZE)] # This dictionary maps the batch data (as a numpy array) to the # node in the graph it should be fed to. feed_dict = {train_data_node: batch_data, train_labels_node: batch_labels} # Run the optimizer to update weights. sess.run(optimizer, feed_dict=feed_dict) # print some extra information once reach the evaluation frequency if step % EVAL_FREQUENCY == 0: # fetch some extra nodes' data l, lr, predictions = sess.run([loss, learning_rate, train_prediction], feed_dict=feed_dict) elapsed_time = time.time() - start_time start_time = time.time() print('Step %d (epoch %.2f), %.1f ms' %(step, float(step) * BATCH_SIZE / train_size, 1000 * elapsed_time / EVAL_FREQUENCY)) print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr)) print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels)) print('Validation error: %.1f%%' % error_rate(eval_in_batches(validation_data, sess), validation_labels)) sys.stdout.flush() # Finally print the result! test_error = error_rate(eval_in_batches(test_data, sess), test_labels) print('Test error: %.1f%%' % test_error) if FLAGS.self_test: print('test_error', test_error) assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (test_error,) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( '--use_fp16', default=False, help='Use half floats instead of full floats if True.', action='store_true') parser.add_argument( '--self_test', default=False, action='store_true', help='True if running a self test.') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
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