使用来自Keras模型的张量流图进行预测

使用来自Keras模型的张量流图进行预测

@frankyjuang将我链接到这里

https://github.com/amir-abdi/keras_to_tensorflow

并将其与来自

https://github.com/metaflow-ai/blog/blob/master/tf-
freeze/load.py

和

https://github.com/tensorflow/tensorflow/issues/675

我找到了既可以使用tf图进行预测又可以创建jacobian函数的解决方案：

import tensorflow as tfimport numpy as np# Create function to convert saved keras model to tensorflow graphdef convert_to_pb(weight_file,input_fld='',output_fld=''):    import os    import os.path as osp    from tensorflow.python.framework import graph_util    from tensorflow.python.framework import graph_io    from keras.models import load_model    from keras import backend as K    # weight_file is a .h5 keras model file    output_node_names_of_input_network = ["pred0"]     output_node_names_of_final_network = 'output_node'    # change filename to a .pb tensorflow file    output_graph_name = weight_file[:-2]+'pb'    weight_file_path = osp.join(input_fld, weight_file)    net_model = load_model(weight_file_path)    num_output = len(output_node_names_of_input_network)    pred = [None]*num_output    pred_node_names = [None]*num_output    for i in range(num_output):        pred_node_names[i] = output_node_names_of_final_network+str(i)        pred[i] = tf.identity(net_model.output[i], name=pred_node_names[i])    sess = K.get_session()    constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), pred_node_names)    graph_io.write_graph(constant_graph, output_fld, output_graph_name, as_text=False)    print('saved the constant graph (ready for inference) at: ', osp.join(output_fld, output_graph_name))    return output_fld+output_graph_name

呼叫：

tf_model_path = convert_to_pb('model_file.h5','/model_dir/','/model_dir/')

创建函数以将tf模型加载为图形：

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        )    input_name = graph.get_operations()[0].name+':0'    output_name = graph.get_operations()[-1].name+':0'    return graph, input_name, output_name

创建一个函数以使用tf图进行模型预测

def predict(model_path, input_data):    # load tf graph    tf_model,tf_input,tf_output = load_graph(model_path)    # Create tensors for model input and output    x = tf_model.get_tensor_by_name(tf_input)    y = tf_model.get_tensor_by_name(tf_output)    # Number of model outputs    num_outputs = y.shape.as_list()[0]    predictions = np.zeros((input_data.shape[0],num_outputs))    for i in range(input_data.shape[0]):     with tf.Session(graph=tf_model) as sess: y_out = sess.run(y, feed_dict={x: input_data[i:i+1]}) predictions[i] = y_out    return predictions

作出预测：

tf_predictions = predict(tf_model_path,test_data)

雅可比函数：

def compute_jacobian(model_path,input_data):    tf_model,tf_input,tf_output = load_graph(model_path)    x = tf_model.get_tensor_by_name(tf_input)    y = tf_model.get_tensor_by_name(tf_output)    y_list = tf.unstack(y)    num_outputs = y.shape.as_list()[0]    jacobian = np.zeros((num_outputs,input_data.shape[0],input_data.shape[1]))    for i in range(input_data.shape[0]):        with tf.Session(graph=tf_model) as sess: y_out = sess.run([tf.gradients(y_, x)[0] for y_ in y_list], feed_dict={x: input_data[i:i+1]}) jac_temp = np.asarray(y_out)        jacobian[:,i:i+1,:]=jac_temp[:,:,:,0]    return jacobian

计算雅可比矩阵：

jacobians = compute_jacobian(tf_model_path,test_data)