io.IOUtils; import org.tensorflow.Graph; import org.tensorflow.Session; import org.tensorflow.Tensor; import java.io.FileInputStream; import java.io.IOException; public class PBFileAnalizy { public static void main(String[] args) throws IOException { // byte[] graphDef = loadTensorflowModel("...
input_tensor = sess.graph.get_tensor_by_name('InputData/X:0') probs = sess.run(op,feed_dict = {input_tensor:temp_image}) print probs result = [] for word in probs: result.append(np.argsort(-word)[:3]) return result def main(_): image_path = './data/test/00098/104405.png...
def import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None, producer_op_list=None): 1. 该函数可加载已存储的”graph_def”到当前默认图里,并从系列化的tensorflow [GraphDef]协议缓冲里提取所有的tf.Tensor和tf.Operation到当前图里,其参数如下: graph_def:一个包...
实现由ckpt文件如何转换为pb文件，再去探索如何在训练时直接保存pb文件，最后是如何利用pb文件复现网络与...
name) \ .feed(utils.Caffe2TensorToNumpyArray(param)) external_input = net.op[0].input[0] ws.create_blob(external_input).feed(dummy_input) # Get dimensions with legacy pad for i in range(len(net.op)): op_def = net.op[i] ws._run_operator(op_def.SerializeToString()) if i in ...
input_checkpoint) var_to_shape_map = reader.get_variable_to_shape_map()forkeyinvar_to_shape_map:try: tensor = sess.graph.get_tensor_by_name(key +':0')exceptKeyError:# This tensor doesn't exist in the graph (for example# it's 'global_step' or a similar housekeeping element)# so ...
'keep_prob': tf.saved_model.utils.build_tensor_info(keep_prob)} # y 为最终需要的输出结果tensor outputs = {'output' : tf.saved_model.utils.build_tensor_info(y)} signature = tf.saved_model.signature_def_utils.build_signature_def(inputs, outputs, 'test_sig_name') ...
(graph_def,name="")seq_wordids=sess.graph.get_tensor_by_name("seq_wordids:0")predict_probs=sess.graph.get_tensor_by_name("predict_probs:0")sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]=\tf.saved_model.signature_def_utils.predict_signature_def({'seq_wordids':seq_wordids}...
(graph_def, name="") g = tf.get_default_graph() inp = g.get_tensor_by_name(input_name) out = g.get_tensor_by_name(output_name) # 重写签名 sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \ tf.saved_model.signature_def_utils.predict_signature_def( {"input": ...
作者首先介绍了Google Cloud Platform的特点和优势，然后详细讲解了如何利用TensorFlow和Keras在Google Cloud ...