from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse from datetime import datetime import math import sys import time from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf FLAGS = None def print_activations(t): print(t.op.name, ' ', t.get_shape().as_list()) def inference(images): """Build the AlexNet model. Args: images: Images Tensor Returns: pool5: the last Tensor in the convolutional component of AlexNet. parameters: a list of Tensors corresponding to the weights and biases of the AlexNet model. """ parameters = [] # conv1 with tf.name_scope('conv1') as scope: kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 96], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(images, kernel, [1, 4, 4, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[96], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(bias, name=scope) print_activations(conv1) parameters += [kernel, biases] # lrn1 # TODO(shlens, jiayq): Add a GPU version of local response normalization. # pool1 pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool1') print_activations(pool1) # conv2 with tf.name_scope('conv2') as scope: kernel = tf.Variable(tf.truncated_normal([5, 5, 96, 256], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases) conv2 = tf.nn.relu(bias, name=scope) parameters += [kernel, biases] print_activations(conv2) # pool2 pool2 = tf.nn.max_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool2') print_activations(pool2) # conv3 with tf.name_scope('conv3') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 384], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[384], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases) conv3 = tf.nn.relu(bias, name=scope) parameters += [kernel, biases] print_activations(conv3) # conv4 with tf.name_scope('conv4') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 384], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[384], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases) conv4 = tf.nn.relu(bias, name=scope) parameters += [kernel, biases] print_activations(conv4) # conv5 with tf.name_scope('conv5') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 256], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases) conv5 = tf.nn.relu(bias, name=scope) parameters += [kernel, biases] print_activations(conv5) # pool5 pool5 = tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool5') print_activations(pool5) return pool5, parameters def time_tensorflow_run(session, target, info_string): """Run the computation to obtain the target tensor and print timing stats. Args: session: the TensorFlow session to run the computation under. target: the target Tensor that is passed to the session's run() function. info_string: a string summarizing this run, to be printed with the stats. Returns: None """ num_steps_burn_in = 10 total_duration = 0.0 total_duration_squared = 0.0 for i in xrange(FLAGS.num_batches + num_steps_burn_in): start_time = time.time() _ = session.run(target) duration = time.time() - start_time if i >= num_steps_burn_in: if not i % 10: print ('%s: step %d, duration = %.3f' % (datetime.now(), i - num_steps_burn_in, duration)) total_duration += duration total_duration_squared += duration * duration mn = total_duration / FLAGS.num_batches vr = total_duration_squared / FLAGS.num_batches - mn * mn sd = math.sqrt(vr) print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' % (datetime.now(), info_string, FLAGS.num_batches, mn, sd)) def run_benchmark(): """Run the benchmark on AlexNet.""" with tf.Graph().as_default(): # Generate some dummy images. image_size = 224 # Note that our padding definition is slightly different the cuda-convnet. # In order to force the model to start with the same activations sizes, # we add 3 to the image_size and employ VALID padding above. images = tf.Variable(tf.random_normal([FLAGS.batch_size, image_size, image_size, 3], dtype=tf.float32, stddev=1e-1)) # Build a Graph that computes the logits predictions from the # inference model. pool5, parameters = inference(images) # Build an initialization operation. init = tf.global_variables_initializer() # Start running operations on the Graph. config = tf.ConfigProto() config.gpu_options.allocator_type = 'BFC' sess = tf.Session(config=config) sess.run(init) # Run the forward benchmark. time_tensorflow_run(sess, pool5, "Forward") # Add a simple objective so we can calculate the backward pass. objective = tf.nn.l2_loss(pool5) # Compute the gradient with respect to all the parameters. grad = tf.gradients(objective, parameters) # Run the backward benchmark. time_tensorflow_run(sess, grad, "Forward-backward") def main(_): run_benchmark() if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( '--batch_size', type=int, default=128, help='Batch size.' ) parser.add_argument( '--num_batches', type=int, default=100, help='Number of batches to run.' ) FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) 文章来源: tensorflow alexnet实现