TensorflowのInceptionV3を使用した予測

Question

私は自分自身のデータセットでテンソルフローのInceptionV3モデルを訓練しました。私はチェックポイントファイルとグラフ（.meta）をトレーニングから持っています。私はこれらのファイルを使って新しい画像のラベルを分類しています。 TFslimから

inception_v3コード

def inception_v3(inputs, 
 
       dropout_keep_prob=0.8, 
 
       num_classes=1000, 
 
       is_training=True, 
 
       restore_logits=True, 
 
       scope=''): 
 
    """Latest Inception from http://arxiv.org/abs/1512.00567. 
 
    "Rethinking the Inception Architecture for Computer Vision" 
 
    Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, 
 
    Zbigniew Wojna 
 
    Args: 
 
    inputs: a tensor of size [batch_size, height, width, channels]. 
 
    dropout_keep_prob: dropout keep_prob. 
 
    num_classes: number of predicted classes. 
 
    is_training: whether is training or not. 
 
    restore_logits: whether or not the logits layers should be restored. 
 
     Useful for fine-tuning a model with different num_classes. 
 
    scope: Optional scope for name_scope. 
 
    Returns: 
 
    a list containing 'logits', 'aux_logits' Tensors. 
 
    """ 
 
    # end_points will collect relevant activations for external use, for example 
 
    # summaries or losses. 
 
    end_points = {} 
 
    with tf.name_scope(scope, 'inception_v3', [inputs]): 
 
    with scopes.arg_scope([ops.conv2d, ops.fc, ops.batch_norm, ops.dropout], 
 
          is_training=is_training): 
 
     with scopes.arg_scope([ops.conv2d, ops.max_pool, ops.avg_pool], 
 
          stride=1, padding='VALID'): 
 
     # 299 x 299 x 3 
 
     end_points['conv0'] = ops.conv2d(inputs, 32, [3, 3], stride=2, 
 
             scope='conv0') 
 
     # 149 x 149 x 32 
 
     end_points['conv1'] = ops.conv2d(end_points['conv0'], 32, [3, 3], 
 
             scope='conv1') 
 
     # 147 x 147 x 32 
 
     end_points['conv2'] = ops.conv2d(end_points['conv1'], 64, [3, 3], 
 
             padding='SAME', scope='conv2') 
 
     # 147 x 147 x 64 
 
     end_points['pool1'] = ops.max_pool(end_points['conv2'], [3, 3], 
 
              stride=2, scope='pool1') 
 
     # 73 x 73 x 64 
 
     end_points['conv3'] = ops.conv2d(end_points['pool1'], 80, [1, 1], 
 
             scope='conv3') 
 
     # 73 x 73 x 80. 
 
     end_points['conv4'] = ops.conv2d(end_points['conv3'], 192, [3, 3], 
 
             scope='conv4') 
 
     # 71 x 71 x 192. 
 
     end_points['pool2'] = ops.max_pool(end_points['conv4'], [3, 3], 
 
              stride=2, scope='pool2') 
 
     # 35 x 35 x 192. 
 
     net = end_points['pool2'] 
 
     # Inception blocks 
 
     with scopes.arg_scope([ops.conv2d, ops.max_pool, ops.avg_pool], 
 
          stride=1, padding='SAME'): 
 
     # mixed: 35 x 35 x 256. 
 
     with tf.variable_scope('mixed_35x35x256a'): 
 
      with tf.variable_scope('branch1x1'): 
 
      branch1x1 = ops.conv2d(net, 64, [1, 1]) 
 
      with tf.variable_scope('branch5x5'): 
 
      branch5x5 = ops.conv2d(net, 48, [1, 1]) 
 
      branch5x5 = ops.conv2d(branch5x5, 64, [5, 5]) 
 
      with tf.variable_scope('branch3x3dbl'): 
 
      branch3x3dbl = ops.conv2d(net, 64, [1, 1]) 
 
      branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) 
 
      branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) 
 
      with tf.variable_scope('branch_pool'): 
 
      branch_pool = ops.avg_pool(net, [3, 3]) 
 
      branch_pool = ops.conv2d(branch_pool, 32, [1, 1]) 
 
      net = tf.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], 3) 
 
      end_points['mixed_35x35x256a'] = net 
 
     # mixed_1: 35 x 35 x 288. 
 
     with tf.variable_scope('mixed_35x35x288a'): 
 
      with tf.variable_scope('branch1x1'): 
 
      branch1x1 = ops.conv2d(net, 64, [1, 1]) 
 
      with tf.variable_scope('branch5x5'): 
 
      branch5x5 = ops.conv2d(net, 48, [1, 1]) 
 
      branch5x5 = ops.conv2d(branch5x5, 64, [5, 5]) 
 
      with tf.variable_scope('branch3x3dbl'): 
 
      branch3x3dbl = ops.conv2d(net, 64, [1, 1]) 
 
      branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) 
 
      branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) 
 
      with tf.variable_scope('branch_pool'): 
 
      branch_pool = ops.avg_pool(net, [3, 3]) 
 
      branch_pool = ops.conv2d(branch_pool, 64, [1, 1]) 
 
      net = tf.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], 3) 
 
      end_points['mixed_35x35x288a'] = net 
 
     # mixed_2: 35 x 35 x 288. 
 
     with tf.variable_scope('mixed_35x35x288b'): 
 
      with tf.variable_scope('branch1x1'): 
 
      branch1x1 = ops.conv2d(net, 64, [1, 1]) 
 
      with tf.variable_scope('branch5x5'): 
 
      branch5x5 = ops.conv2d(net, 48, [1, 1]) 
 
      branch5x5 = ops.conv2d(branch5x5, 64, [5, 5]) 
 
      with tf.variable_scope('branch3x3dbl'): 
 
      branch3x3dbl = ops.conv2d(net, 64, [1, 1]) 
 
      branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) 
 
      branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) 
 
      with tf.variable_scope('branch_pool'): 
 
      branch_pool = ops.avg_pool(net, [3, 3]) 
 
      branch_pool = ops.conv2d(branch_pool, 64, [1, 1]) 
 
      net = tf.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], 3) 
 
      end_points['mixed_35x35x288b'] = net 
 
     # mixed_3: 17 x 17 x 768. 
 
     with tf.variable_scope('mixed_17x17x768a'): 
 
      with tf.variable_scope('branch3x3'): 
 
      branch3x3 = ops.conv2d(net, 384, [3, 3], stride=2, padding='VALID') 
 
      with tf.variable_scope('branch3x3dbl'): 
 
      branch3x3dbl = ops.conv2d(net, 64, [1, 1]) 
 
      branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3]) 
 
      branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3], 
 
             stride=2, padding='VALID') 
 
      with tf.variable_scope('branch_pool'): 
 
      branch_pool = ops.max_pool(net, [3, 3], stride=2, padding='VALID') 
 
      net = tf.concat([branch3x3, branch3x3dbl, branch_pool], 3) 
 
      end_points['mixed_17x17x768a'] = net 
 
     # mixed4: 17 x 17 x 768. 
 
     with tf.variable_scope('mixed_17x17x768b'): 
 
      with tf.variable_scope('branch1x1'): 
 
      branch1x1 = ops.conv2d(net, 192, [1, 1]) 
 
      with tf.variable_scope('branch7x7'): 
 
      branch7x7 = ops.conv2d(net, 128, [1, 1]) 
 
      branch7x7 = ops.conv2d(branch7x7, 128, [1, 7]) 
 
      branch7x7 = ops.conv2d(branch7x7, 192, [7, 1]) 
 
      with tf.variable_scope('branch7x7dbl'): 
 
      branch7x7dbl = ops.conv2d(net, 128, [1, 1]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 128, [7, 1]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 128, [1, 7]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 128, [7, 1]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7]) 
 
      with tf.variable_scope('branch_pool'): 
 
      branch_pool = ops.avg_pool(net, [3, 3]) 
 
      branch_pool = ops.conv2d(branch_pool, 192, [1, 1]) 
 
      net = tf.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], 3) 
 
      end_points['mixed_17x17x768b'] = net 
 
     # mixed_5: 17 x 17 x 768. 
 
     with tf.variable_scope('mixed_17x17x768c'): 
 
      with tf.variable_scope('branch1x1'): 
 
      branch1x1 = ops.conv2d(net, 192, [1, 1]) 
 
      with tf.variable_scope('branch7x7'): 
 
      branch7x7 = ops.conv2d(net, 160, [1, 1]) 
 
      branch7x7 = ops.conv2d(branch7x7, 160, [1, 7]) 
 
      branch7x7 = ops.conv2d(branch7x7, 192, [7, 1]) 
 
      with tf.variable_scope('branch7x7dbl'): 
 
      branch7x7dbl = ops.conv2d(net, 160, [1, 1]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [7, 1]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [1, 7]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [7, 1]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7]) 
 
      with tf.variable_scope('branch_pool'): 
 
      branch_pool = ops.avg_pool(net, [3, 3]) 
 
      branch_pool = ops.conv2d(branch_pool, 192, [1, 1]) 
 
      net = tf.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], 3) 
 
      end_points['mixed_17x17x768c'] = net 
 
     # mixed_6: 17 x 17 x 768. 
 
     with tf.variable_scope('mixed_17x17x768d'): 
 
      with tf.variable_scope('branch1x1'): 
 
      branch1x1 = ops.conv2d(net, 192, [1, 1]) 
 
      with tf.variable_scope('branch7x7'): 
 
      branch7x7 = ops.conv2d(net, 160, [1, 1]) 
 
      branch7x7 = ops.conv2d(branch7x7, 160, [1, 7]) 
 
      branch7x7 = ops.conv2d(branch7x7, 192, [7, 1]) 
 
      with tf.variable_scope('branch7x7dbl'): 
 
      branch7x7dbl = ops.conv2d(net, 160, [1, 1]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [7, 1]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [1, 7]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [7, 1]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7]) 
 
      with tf.variable_scope('branch_pool'): 
 
      branch_pool = ops.avg_pool(net, [3, 3]) 
 
      branch_pool = ops.conv2d(branch_pool, 192, [1, 1]) 
 
      net = tf.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], 3) 
 
      end_points['mixed_17x17x768d'] = net 
 
     # mixed_7: 17 x 17 x 768. 
 
     with tf.variable_scope('mixed_17x17x768e'): 
 
      with tf.variable_scope('branch1x1'): 
 
      branch1x1 = ops.conv2d(net, 192, [1, 1]) 
 
      with tf.variable_scope('branch7x7'): 
 
      branch7x7 = ops.conv2d(net, 192, [1, 1]) 
 
      branch7x7 = ops.conv2d(branch7x7, 192, [1, 7]) 
 
      branch7x7 = ops.conv2d(branch7x7, 192, [7, 1]) 
 
      with tf.variable_scope('branch7x7dbl'): 
 
      branch7x7dbl = ops.conv2d(net, 192, [1, 1]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [7, 1]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [7, 1]) 
 
      branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7]) 
 
      with tf.variable_scope('branch_pool'): 
 
      branch_pool = ops.avg_pool(net, [3, 3]) 
 
      branch_pool = ops.conv2d(branch_pool, 192, [1, 1]) 
 
      net = tf.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], 3) 
 
      end_points['mixed_17x17x768e'] = net 
 
     # Auxiliary Head logits 
 
     aux_logits = tf.identity(end_points['mixed_17x17x768e']) 
 
     with tf.variable_scope('aux_logits'): 
 
      aux_logits = ops.avg_pool(aux_logits, [5, 5], stride=3, 
 
            padding='VALID') 
 
      aux_logits = ops.conv2d(aux_logits, 128, [1, 1], scope='proj') 
 
      # Shape of feature map before the final layer. 
 
      shape = aux_logits.get_shape() 
 
      aux_logits = ops.conv2d(aux_logits, 768, shape[1:3], stddev=0.01, 
 
            padding='VALID') 
 
      aux_logits = ops.flatten(aux_logits) 
 
      aux_logits = ops.fc(aux_logits, num_classes, activation=None, 
 
           stddev=0.001, restore=restore_logits) 
 
      end_points['aux_logits'] = aux_logits 
 
     # mixed_8: 8 x 8 x 1280. 
 
     # Note that the scope below is not changed to not void previous 
 
     # checkpoints. 
 
     # (TODO) Fix the scope when appropriate. 
 
     with tf.variable_scope('mixed_17x17x1280a'): 
 
      with tf.variable_scope('branch3x3'): 
 
      branch3x3 = ops.conv2d(net, 192, [1, 1]) 
 
      branch3x3 = ops.conv2d(branch3x3, 320, [3, 3], stride=2, 
 
            padding='VALID') 
 
      with tf.variable_scope('branch7x7x3'): 
 
      branch7x7x3 = ops.conv2d(net, 192, [1, 1]) 
 
      branch7x7x3 = ops.conv2d(branch7x7x3, 192, [1, 7]) 
 
      branch7x7x3 = ops.conv2d(branch7x7x3, 192, [7, 1]) 
 
      branch7x7x3 = ops.conv2d(branch7x7x3, 192, [3, 3], 
 
            stride=2, padding='VALID') 
 
      with tf.variable_scope('branch_pool'): 
 
      branch_pool = ops.max_pool(net, [3, 3], stride=2, padding='VALID') 
 
      net = tf.concat([branch3x3, branch7x7x3, branch_pool], 3) 
 
      end_points['mixed_17x17x1280a'] = net 
 
     # mixed_9: 8 x 8 x 2048. 
 
     with tf.variable_scope('mixed_8x8x2048a'): 
 
      with tf.variable_scope('branch1x1'): 
 
      branch1x1 = ops.conv2d(net, 320, [1, 1]) 
 
      with tf.variable_scope('branch3x3'): 
 
      branch3x3 = ops.conv2d(net, 384, [1, 1]) 
 
      branch3x3 = tf.concat([ops.conv2d(branch3x3, 384, [1, 3]), 
 
            ops.conv2d(branch3x3, 384, [3, 1])], 3) 
 
      with tf.variable_scope('branch3x3dbl'): 
 
      branch3x3dbl = ops.conv2d(net, 448, [1, 1]) 
 
      branch3x3dbl = ops.conv2d(branch3x3dbl, 384, [3, 3]) 
 
      branch3x3dbl = tf.concat([ops.conv2d(branch3x3dbl, 384, [1, 3]), 
 
             ops.conv2d(branch3x3dbl, 384, [3, 1])], 3) 
 
      with tf.variable_scope('branch_pool'): 
 
      branch_pool = ops.avg_pool(net, [3, 3]) 
 
      branch_pool = ops.conv2d(branch_pool, 192, [1, 1]) 
 
      net = tf.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], 3) 
 
      end_points['mixed_8x8x2048a'] = net 
 
     # mixed_10: 8 x 8 x 2048. 
 
     with tf.variable_scope('mixed_8x8x2048b'): 
 
      with tf.variable_scope('branch1x1'): 
 
      branch1x1 = ops.conv2d(net, 320, [1, 1]) 
 
      with tf.variable_scope('branch3x3'): 
 
      branch3x3 = ops.conv2d(net, 384, [1, 1]) 
 
      branch3x3 = tf.concat([ops.conv2d(branch3x3, 384, [1, 3]), 
 
            ops.conv2d(branch3x3, 384, [3, 1])], 3) 
 
      with tf.variable_scope('branch3x3dbl'): 
 
      branch3x3dbl = ops.conv2d(net, 448, [1, 1]) 
 
      branch3x3dbl = ops.conv2d(branch3x3dbl, 384, [3, 3]) 
 
      branch3x3dbl = tf.concat([ops.conv2d(branch3x3dbl, 384, [1, 3]), 
 
             ops.conv2d(branch3x3dbl, 384, [3, 1])], 3) 
 
      with tf.variable_scope('branch_pool'): 
 
      branch_pool = ops.avg_pool(net, [3, 3]) 
 
      branch_pool = ops.conv2d(branch_pool, 192, [1, 1]) 
 
      net = tf.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], 3) 
 
      end_points['mixed_8x8x2048b'] = net 
 
     # Final pooling and prediction 
 
     with tf.variable_scope('logits'): 
 
      shape = net.get_shape() 
 
      net = ops.avg_pool(net, shape[1:3], padding='VALID', scope='pool') 
 
      # 1 x 1 x 2048 
 
      net = ops.dropout(net, dropout_keep_prob, scope='dropout') 
 
      net = ops.flatten(net, scope='flatten') 
 
      # 2048 
 
      logits = ops.fc(net, num_classes, activation=None, scope='logits', 
 
          restore=restore_logits) 
 
      # 1000 
 
      end_points['logits'] = logits 
 
      end_points['predictions'] = tf.nn.softmax(logits, name='predictions') 
 
     return logits, end_points

予測コード

config = tf.ConfigProto(allow_soft_placement=True) 
 
saver = tf.train.import_meta_graph('path/to/meta/graph') 
 
graph = tf.get_default_graph() 
 
with tf.Session(config=config,graph=graph) as sess: 
 
     print graph 
 
     saver.restore(sess,'/path/to/chpk/') 
 
     init_op = tf.group(tf.initialize_all_variables(), tf.initialize_local_variables()) 
 
     sess.run(init_op) 
 
     print ('Restored checkpoint file and graph') 
 
     tens=image_preprocessing(tf.read_file('/serving/13_left.jpeg')) 
 
     with slim.arg_scope(inception_arg_scope()): 
 
       logits = inception_v3(tf.expand_dims(tens,0), 
 
           num_classes=6, 
 
           is_training=False) 
 
     prob = tf.nn.softmax(logits) 
 
     sess.run(prob)

：今まで私は、次のを持っています

FailedPreconditionError (see above for traceback): Attempting to use uninitialized value mixed_17x17x768d/branch7x7dbl/Conv_3/weights_2 
 
\t [[Node: mixed_17x17x768d/branch7x7dbl/Conv_3/weights_2/read = Identity[T=DT_FLOAT, _class=["loc:@mixed_17x17x768d/branch7x7dbl/Conv_3/weights_2"], _device="/job:localhost/replica:0/task:0/cpu:0"](mixed_17x17x768d/branch7x7dbl/Conv_3/weights_2)]]

私はので、ここで任意のヘルプははるかに高く評価されTensorflowすることはかなり新しいです：

この

は、次のエラーが発生します。私は間違ったことをしていますが、それを理解できません。事前に感謝:)

EDIT 1

私はセッションにグラフと重みを再ロードすることによって、私のセッションを再開しました。グラフをフリーズしてfrozen_graph.pbという名前で保存しました。以下の私のコードです：

def freeze_graph(model_folder): 
 
    # We retrieve our checkpoint fullpath 
 
    checkpoint = tf.train.get_checkpoint_state(model_folder) 
 
    input_checkpoint = checkpoint.model_checkpoint_path 
 

 
    # We precise the file fullname of our freezed graph 
 
    absolute_model_folder = "/".join(input_checkpoint.split('/')[:-1]) 
 
    output_graph = absolute_model_folder + "/frozen_model.pb" 
 

 
    # Before exporting our graph, we need to precise what is our output node 
 
    # This is how TF decides what part of the Graph he has to keep and what part it can dump 
 
    output_node_names = "tower_0/logits/predictions" 
 

 
    # We clear devices to allow TensorFlow to control on which device it will load operations 
 
    clear_devices = True 
 

 
    # We import the meta graph and retrieve a Saver 
 
    saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices) 
 

 
    # We retrieve the protobuf graph definition 
 
    graph = tf.get_default_graph() 
 
    input_graph_def = graph.as_graph_def() 
 

 
    # We start a session and restore the graph weights 
 
    with tf.Session() as sess: 
 
     saver.restore(sess, input_checkpoint) 
 
     for op in sess.graph.get_operations(): 
 
       print(op.name) 
 
     # We use a built-in TF helper to export variables to constants 
 
     output_graph_def = graph_util.convert_variables_to_constants(
 
      sess, # The session is used to retrieve the weights 
 
      input_graph_def # The graph_def is used to retrieve the nodes 
 
      ,output_node_names.split(",")) # The output node names are used to select the usefull nodes 
 

 

 
     # Finally we serialize and dump the output graph to the filesystem 
 
     with tf.gfile.GFile(output_graph, "wb") as f: 
 
      f.write(output_graph_def.SerializeToString()) 
 
     print("%d ops in the final graph." % len(output_graph_def.node)) 
 

 

 
if __name__ == '__main__': 
 
    parser = argparse.ArgumentParser() 
 
    parser.add_argument("--model_folder", type=str, help="Model folder to export") 
 
    args = parser.parse_args() 
 
    freeze_graph(args.model_folder)

私は新しいセッションに凍結されたグラフをロードします。

prefix/batch_processing/batch_join/fifo_queue 
 
prefix/batch_processing/batch_join/n 
 
prefix/batch_processing/batch_join 
 
prefix/batch_processing/Reshape/shape 
 
prefix/batch_processing/Reshape 
 
. 
 
. 
 
. 
 
prefix/logits/logits/weights 
 
prefix/logits/logits/weights/read 
 
prefix/logits/logits/biases 
 
prefix/logits/logits/biases/read 
 
prefix/tower_0/logits/logits/xw_plus_b/MatMul 
 
prefix/tower_0/logits/logits/xw_plus_b 
 
prefix/tower_0/logits/predictions

私は新しいイメージを分類するための入力ノードと最終ノード（予測）を使用します。以下は、私のノード名です。続いて、このために私のコードです：

def load_graph(frozen_graph_filename): 
 
    # We load the protobuf file from the disk and parse it to retrieve the 
 
    # unserialized graph_def 
 
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f: 
 
     graph_def = tf.GraphDef() 
 
     graph_def.ParseFromString(f.read()) 
 

 
    # Then, we can use again a convenient built-in function to import a graph_def into the 
 
    # current default Graph 
 
    with tf.Graph().as_default() as graph: 
 
     tf.import_graph_def(
 
      graph_def, 
 
      input_map=None, 
 
      return_elements=None, 
 
      name="prefix", 
 
      op_dict=None, 
 
      producer_op_list=None 
 
     ) 
 
    return graph 
 

 
#graph = load_graph('/serving/frozen_model.pb') 
 

 
if __name__ == '__main__': 
 
    import scipy.misc 
 
    # Let's allow the user to pass the filename as an argument 
 
    parser = argparse.ArgumentParser() 
 
    parser.add_argument("--frozen_model_filename", default="/serving/frozen_model.pb", type=str, help="Frozen model file to import") 
 
    args = parser.parse_args() 
 

 
    # We use our "load_graph" function 
 
    graph = load_graph(args.frozen_model_filename) 
 

 
    # We can verify that we can access the list of operations in the graph 
 
    for op in graph.get_operations(): 
 
     print(op.name) 
 
     # prefix/Placeholder/inputs_placeholder 
 
     # ... 
 
     # prefix/Accuracy/predictions 
 

 
    # We access the input and output nodes 
 
    x = graph.get_tensor_by_name('prefix/batch_processing/batch_join/fifo_queue:0') 
 
    y = graph.get_tensor_by_name('prefix/tower_0/logits/predictions:0') 
 
    image_data = tf.gfile.FastGFile('/serving/13_left.jpeg', 'rb').read() 
 
    # We launch a Session 
 
    with tf.Session(graph=graph) as sess: 
 
     # Note: we didn't initialize/restore anything, everything is stored in the graph_def 
 
     y_out = sess.run(y, feed_dict={ 
 
      x: image_data}) # < 45 
 

 
     print(y_out) # [[ False ]] Yay, it works!

私は次のエラーを取得します。

Traceback (most recent call last): 
 
    File "predict_3.py", line 183, in <module> 
 
    x: image_data}) # < 45 
 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 767, in run 
 
    run_metadata_ptr) 
 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 929, in _run 
 
    subfeed_dtype = subfeed_t.dtype.as_numpy_dtype 
 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/dtypes.py", line 138, in as_numpy_dtype 
 
    return _TF_TO_NP[self._type_enum] 
 
KeyError: 20

今ではありません間違って何が起こっているのを確認してください。私は自分のノードが正しいと思う。これに関する助けは大いに感謝しています。

Answer 1

最後に、この問題を解決する方法を見つけました。予測のためにノードを再利用できる.pbファイルを生成するために、グラフをフリーズする必要があります。

def freeze_graph(model_folder): 
 
    # We retrieve our checkpoint fullpath 
 
    checkpoint = tf.train.get_checkpoint_state(model_folder) 
 
    input_checkpoint = checkpoint.model_checkpoint_path 
 

 
    # We precise the file fullname of our freezed graph 
 
    absolute_model_folder = "/".join(input_checkpoint.split('/')[:-1]) 
 
    output_graph = absolute_model_folder + "/frozen_model.pb" 
 

 
    # Before exporting our graph, we need to precise what is our output node 
 
    # This is how TF decides what part of the Graph he has to keep and what part it can dump 
 
    # NOTE: this variable is plural, because you can have multiple output nodes 
 
    output_node_names = "tower_0/logits/predictions" 
 

 
    # We clear devices to allow TensorFlow to control on which device it will load operations 
 
    clear_devices = True 
 

 
    # We import the meta graph and retrieve a Saver 
 
    saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices) 
 

 
    # We retrieve the protobuf graph definition 
 
    graph = tf.get_default_graph() 
 
    input_graph_def = graph.as_graph_def() 
 

 
    # We start a session and restore the graph weights 
 
    with tf.Session() as sess: 
 
     saver.restore(sess, input_checkpoint) 
 
     for op in sess.graph.get_operations(): 
 
       print(op.name) 
 
     # We use a built-in TF helper to export variables to constants 
 
     output_graph_def = graph_util.convert_variables_to_constants(
 
      sess, # The session is used to retrieve the weights 
 
      input_graph_def # The graph_def is used to retrieve the nodes 
 
      ,output_node_names.split(",")) # The output node names are used to select the usefull nodes 
 

 

 
     # Finally we serialize and dump the output graph to the filesystem 
 
     with tf.gfile.GFile(output_graph, "wb") as f: 
 
      f.write(output_graph_def.SerializeToString()) 
 
     print("%d ops in the final graph." % len(output_graph_def.node)) 
 

 

 
if __name__ == '__main__': 
 
    parser = argparse.ArgumentParser() 
 
    parser.add_argument("--model_folder", type=str, help="Model folder to export") 
 
    args = parser.parse_args() 
 

 
    freeze_graph(args.model_folder)

この後、我々はグラフを読み込むことができます。次はこれを行うに私のコードです。続いて、このために私のコードです：これはあなたのプロトコルバッファ（frozen_graph.pb）を作成します

def load_graph(frozen_graph_filename): 
 
    # We load the protobuf file from the disk and parse it to retrieve the 
 
    # unserialized graph_def 
 
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f: 
 
     graph_def = tf.GraphDef() 
 
     graph_def.ParseFromString(f.read()) 
 

 
    # Then, we can use again a convenient built-in function to import a graph_def into the 
 
    # current default Graph 
 
    with tf.Graph().as_default() as graph: 
 
     tf.import_graph_def(
 
      graph_def, 
 
      input_map=None, 
 
      return_elements=None, 
 
      name="prefix", 
 
      op_dict=None, 
 
      producer_op_list=None 
 
     ) 
 
    return graph

た私達ができるLO

if __name__ == '__main__': 
 
    # Let's allow the user to pass the filename as an argument 
 
    parser = argparse.ArgumentParser() 
 
    parser.add_argument("--frozen_model_filename", default="/serving/frozen_model.pb", type=str, help="Frozen model file to import") 
 
    parser.add_argument("--image_name",type=str,help="Image to test") 
 
    args = parser.parse_args() 
 

 
    # Create test batch 
 
    image_data = create_test_batch(args.image_name) 
 
    # We use our "load_graph" function 
 
    graph = load_graph(args.frozen_model_filename) 
 

 
    # We can verify that we can access the list of operations in the graph 
 
    #for op in graph.get_operations(): 
 
     #print(op.name) 
 
     # prefix/Placeholder/inputs_placeholder 
 
     # ... 
 
     # prefix/Accuracy/predictions 
 

 
    # We access the input and output nodes 
 
    x = graph.get_tensor_by_name('prefix/batch_processing/Reshape:0') # Input tensor 
 
    y = graph.get_tensor_by_name('prefix/tower_0/logits/predictions:0') # Output tensor 
 

 
    # We launch a Session 
 
    with tf.Session(graph=graph) as sess: 
 
     # Note: we didn't initialize/restore anything, everything is stored in the graph_def 
 
     y_out = sess.run(y, feed_dict={ 
 
      x:image_data}) # < 45  
 
     print(y_out)

マイ入力ノード（x）はサイズ64とサイズ299x299x3のバッチを期待しているので、私は自分の道をハックしたテストイメージを64回作成し、入力バッチを作成します。私は、次の方法でこれを実行します。

def create_test_batch(input_image): 
 
     data = [] 
 
     img = cv2.imread(input_image) # Read the test image 
 
     img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV) # Convert RGB image to YUV 
 
     # equalize the histogram of the Y channel 
 
     img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0]) 
 
     # convert the YUV image back to RGB format 
 
     img_output = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR) 
 
     img_resize = cv2.resize(img_output,(299,299)) # Resize the image acceptable by InceptionV3 model 
 
     for i in range(0,64): 
 
       data.append(img_resize) # Create a batch of 64 images 
 
       #data.append(np.resize((ndimage.imread('/serving/'+input_image)),(299,299,3))) 
 
     print np.shape(data) 
 
     return data

入力バッチ問題を解決するためのより良い方法があれば、私は答えを感謝します。