tensorflow35《TensorFlow实战》笔记-06-03 TensorFlow实现 GoogleInceptionV3 code

# 《TensorFlow实战》06 TensorFlow实现经典卷积神经网络# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:sz06.03.py # TensorFlow实现 Google Inception V3# https://github.com/tensorflow/models/blob/master/slim/nets/inception_v3.py# tensorflow_models\slim\nets\inception_v3.pyimport tensorflow as tffrom datetime import datetimeimport mathimport timeslim = tf.contrib.slimtrunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)def inception_v3_arg_scope(weight_decay=0.00004, stddev=0.1, batch_norm_var_collection='moving_vars'):    batch_norm_params = {        'decay': 0.9997,        'epsilon': 0.001,        'updates_collections': tf.GraphKeys.UPDATE_OPS,        'variables_collections': {            'beta': None,            'gamma': None,            'moving_mean': [batch_norm_var_collection],            'moving_variance': [batch_norm_var_collection],        }    }    with slim.arg_scope([slim.conv2d, slim.fully_connected],        weights_regularizer = slim.l2_regularizer(weight_decay)):        with slim.arg_scope(            [slim.conv2d],            weights_initializer=tf.truncated_normal_initializer(stddev=stddev),            activation_fn = tf.nn.relu,            normalizer_fn = slim.batch_norm,            normalizer_params = batch_norm_params) as sc:            return scdef inception_v3_base(inputs, scope=None):    end_points = {}    with tf.variable_scope(scope, 'InceptionsV3', [inputs]):        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],                            stride=1, padding="VALID"):            net = slim.conv2d(inputs, 32, [3, 3], stride=2, scope='Conv2d_1a_3x3')            net = slim.conv2d(net, 32, [3, 3], scope='Conv2d_2a_3x3')            net = slim.conv2d(net, 64, [3, 3], padding='SAME', scope='Conv2d_2b_3x3')            net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_3a_3x3')            net = slim.conv2d(net, 80, [1, 1], scope='Conv2d_3b_1x1')            net = slim.conv2d(net, 192, [3, 3], scope='Conv2d_4a_3x3')            net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_5a_3x3')        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],                            stride=1, padding='SAME'):            with tf.variable_scope('Mixed_5b'):                with tf.variable_scope('Branch_0'):                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')                with tf.variable_scope('Branch_1'):                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')                with tf.variable_scope('Branch_2'):                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')                with tf.variable_scope('Branch_3'):                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')                    branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)            with tf.variable_scope('Mixed_5c'):                with tf.variable_scope('Branch_0'):                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')                with tf.variable_scope('Branch_1'):                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0b_1x1')                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0c_5x5')                with tf.variable_scope('Branch_2'):                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')                with tf.variable_scope('Branch_3'):                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')                    branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)            with tf.variable_scope('Mixed_5d'):                with tf.variable_scope('Branch_0'):                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')                with tf.variable_scope('Branch_1'):                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0b_1x1')                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')                with tf.variable_scope('Branch_2'):                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')                with tf.variable_scope('Branch_3'):                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='Conv2d_0a_3x3')                    branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)            with tf.variable_scope('Mixed_6a'):                with tf.variable_scope('Branch_0'):                    branch_0 = slim.conv2d(net, 384, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_1x1')                with tf.variable_scope('Branch_1'):                    branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')                    branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')                    branch_1 = slim.conv2d(branch_1, 96, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_1x1')                with tf.variable_scope('Branch_2'):                    branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')                net = tf.concat([branch_0, branch_1, branch_2], 3)            with tf.variable_scope('Mixed_6b'):                with tf.variable_scope('Branch_0'):                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')                with tf.variable_scope('Branch_1'):                    branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')                    branch_1 = slim.conv2d(branch_1, 128, [1, 7], scope='Conv2d_0b_1x7')                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')                with tf.variable_scope('Branch_2'):                    branch_2 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')                    branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0b_7x1')                    branch_2 = slim.conv2d(branch_2, 128, [1, 7], scope='Conv2d_0c_1x7')                    branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0d_7x1')                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')                with tf.variable_scope('Branch_3'):                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)            with tf.variable_scope('Mixed_6c'):                with tf.variable_scope('Branch_0'):                    branch_0 = slim.conv2d(net, 192, [1, 1], scope="Conv2d_0a_1x1")                with tf.variable_scope('Branch_1'):                    branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')                    branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')                with tf.variable_scope('Branch_2'):                    branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')                    branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')                with tf.variable_scope('Branch_3'):                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)            with tf.variable_scope('Mixed_6d'):                with tf.variable_scope('Branch_0'):                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')                with tf.variable_scope('Branch_1'):                    branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')                    branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')                with tf.variable_scope('Branch_2'):                    branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')                    branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')                with tf.variable_scope('Branch_3'):                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)            with tf.variable_scope('Mixed_6e'):                with tf.variable_scope('Branch_0'):                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')                with tf.variable_scope('Branch_1'):                    branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')                    branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv2d_0b_1x7')                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')                with tf.variable_scope('Branch_2'):                    branch_2 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')                    branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1')                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0c_1x7')                    branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0d_1x7')                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_03_1x7')                with tf.variable_scope('Branch_3'):                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)            end_points['Mixed_6e'] = net            with tf.variable_scope('Mixed_7a'):                with tf.variable_scope('Branch_0'):                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')                    branch_0 = slim.conv2d(branch_0, 320, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3')                with tf.variable_scope('Branch_1'):                    branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')                    branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv2d_0b_1x7')                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')                    branch_1 = slim.conv2d(branch_1, 192, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3')                with tf.variable_scope('Branch_2'):                    branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')                net = tf.concat([branch_0, branch_1, branch_2], 3)            with tf.variable_scope('Mixed_7b'):                with tf.variable_scope('Branch_0'):                    branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_0a_1x1')                with tf.variable_scope('Branch_1'):                    branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')                    branch_1 = tf.concat([                        slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),                        slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0b_3x1')], 3)                with tf.variable_scope('Branch_2'):                    branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_0a_1x1')                    branch_2 = slim.conv2d(branch_2, 384, [3, 3], scope='Conv2d_0b_3x3')                    branch_2 = tf.concat([                        slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),                        slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')                    ], 3)                with tf.variable_scope('Branch_3'):                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='Conv2d_0a_3x3')                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)            with tf.variable_scope('Mixed_7c'):                with tf.variable_scope('Branch_0'):                    branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_0a_1x1')                with tf.variable_scope('Branch_1'):                    branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')                    branch_1 = tf.concat([                        slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),                        slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0c_3x1')                    ], 3)                with tf.variable_scope('Branch_2'):                    branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_0a_1x1')                    branch_2 = slim.conv2d(branch_2, 384, [3, 3], scope='Conv2d_0b_3x3')                    branch_2 = tf.concat([                        slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),                        slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')                    ], 3)                with tf.variable_scope('Branch_3'):                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)            return net, end_pointsdef inception_v3(inputs,                 num_classes = 10000,                 is_training=True,                 dropout_keep_prob=0.8,                 prediction_fn=slim.softmax,                 spatial_squeeze=True,                 reuse=None,                 scope='InceptionV3'):    with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes], reuse=reuse) as scope:        with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training):            net, end_points = inception_v3_base(inputs, scope=scope)            with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'):                aux_logits = end_points['Mixed_6e']                with tf.variable_scope('AuxLogits'):                    aux_logits = slim.avg_pool2d(aux_logits, [5, 5],                                                 stride=3, padding='VALID', scope='AvgPool_1a_5x5')                    aux_logits = slim.conv2d(aux_logits, 128, [1, 1], scope='Conv2d_1b_1x1')                    aux_logits = slim.conv2d(                        aux_logits, 768, [5, 5],                        weights_initializer=trunc_normal(0.01),                        padding='VALID', scope='Conv2d_2a_5x5'                    )                    aux_logits = slim.conv2d(                        aux_logits, num_classes, [1, 1], activation_fn=None,                        normalizer_fn=None, weights_initializer=trunc_normal(0.001),                        scope='Conv2d_2b_1x1'                    )                    if spatial_squeeze:                        aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze')                    end_points['AuxLogits'] = aux_logits                with tf.variable_scope('Logits'):                    net = slim.avg_pool2d(net, [8, 8], padding='VALID', scope='AvgPool_1a_8x8')                    net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')                    end_points['PreLogits'] = net                    logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,                                             normalizer_fn=None, scope='Conv2d_1c_1x1')                    if spatial_squeeze:                        logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')                end_points['Logits'] = logits                end_points['Predictions'] = prediction_fn(logits, scope='Predictions')            return logits, end_pointsdef time_tensorflow_run(session, target, info_string):    num_steps_burn_in = 10    total_duration = 0.0    total_duration_squared = 0.0    for i in range(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 / num_batches    vr = total_duration_squared / num_batches - mn * mn    sd = math.sqrt(vr)    print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %(datetime.now(), info_string, num_batches, mn, sd))batch_size = 32height, width = 299, 299inputs = tf.random_uniform((batch_size, height, width, 3))with slim.arg_scope(inception_v3_arg_scope()):    logits, end_points = inception_v3(inputs, is_training=False)init = tf.global_variables_initializer()sess = tf.Session()sess.run(init)num_batches = 100time_tensorflow_run(sess, logits, 'Forward')'''2017-04-14 21:54:30.821468: step 0, duration = 0.7192017-04-14 21:54:38.009776: step 10, duration = 0.7192017-04-14 21:54:45.198046: step 20, duration = 0.7192017-04-14 21:54:52.401908: step 30, duration = 0.7192017-04-14 21:54:59.590177: step 40, duration = 0.7192017-04-14 21:55:06.790584: step 50, duration = 0.7092017-04-14 21:55:13.991166: step 60, duration = 0.7192017-04-14 21:55:21.195063: step 70, duration = 0.7192017-04-14 21:55:28.398961: step 80, duration = 0.7342017-04-14 21:55:35.599010: step 90, duration = 0.7192017-04-14 21:55:42.068459: Forward across 100 steps, 0.720 +/- 0.003 sec / batch'''