使用keras模型中的张量流图进行预测 [英] Make predictions using a tensorflow graph from a keras model

问题描述

我有一个使用Tensorflow作为后端的Keras训练的模型，但是现在我需要将我的模型转换为用于特定应用程序的张量流图.我尝试执行此操作并进行预测以确保其正常工作，但是当与从model.predict()收集的结果进行比较时，我得到了非常不同的值.例如:

I have a model trained using Keras with Tensorflow as my backend, but now I need to turn my model into a tensorflow graph for a certain application. I attempted to do this and make predictions to insure that it is working correctly, but when comparing to the results gathered from model.predict() I get very different values. For instance:

from keras.models import load_model
import tensorflow as tf

model = load_model('model_file.h5')

x_placeholder = tf.placeholder(tf.float32, shape=(None,7214,1))
y = model(x_placeholder)

x = np.ones((1,7214,1))


with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print("Predictions from:\ntf graph:      "+str(sess.run(y, feed_dict={x_placeholder:x})))
    print("keras predict: "+str(model.predict(x)))

返回:

Predictions from:
tf graph:      [[-0.1015993   0.07432419  0.0592984 ]]
keras predict: [[ 0.39339241  0.57949686 -3.67846966]]

Keras预测值正确，但tf图结果不正确.

The values from keras predict are correct, but the tf graph results are not.

如果它有助于了解最终的预期应用程序，那么我将使用tf.gradients()函数创建一个jacobian矩阵，但与theano的jacobian函数进行比较(目前提供正确的jacobian函数)时，当前它无法返回正确的结果.这是我的tensorflow jacobian代码:

If it helps to know the final intended application, I am creating a jacobian matrix with the tf.gradients() function, but currently it does not return the correct results when comparing to theano's jacobian function, which gives the correct jacobian. Here is my tensorflow jacobian code:

x = tf.placeholder(tf.float32, shape=(None,7214,1))
y = tf.reshape(model(x)[0],[-1])
y_list = tf.unstack(y)

jacobian_list = [tf.gradients(y_, x)[0] for y_ in y_list]
jacobian = tf.stack(jacobian_list)

模型代码

import numpy as np

from keras.models import Sequential
from keras.layers import Dense, InputLayer, Flatten
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping, ReduceLROnPlateau

# activation function used following every layer except for the output layers
activation = 'relu'

# model weight initializer
initializer = 'he_normal'

# shape of input data that is fed into the input layer
input_shape = (None,7214,1)

# number of filters used in the convolutional layers
num_filters = [4,16]

# length of the filters in the convolutional layers
filter_length = 8

# length of the maxpooling window 
pool_length = 4

# number of nodes in each of the hidden fully connected layers
num_hidden_nodes = [256,128]

# number of samples fed into model at once during training
batch_size = 64

# maximum number of interations for model training
max_epochs = 30

# initial learning rate for optimization algorithm
lr = 0.0007

# exponential decay rate for the 1st moment estimates for optimization algorithm
beta_1 = 0.9

# exponential decay rate for the 2nd moment estimates for optimization algorithm
beta_2 = 0.999

# a small constant for numerical stability for optimization algorithm
optimizer_epsilon = 1e-08

model = Sequential([

    InputLayer(batch_input_shape=input_shape),

    Conv1D(kernel_initializer=initializer, activation=activation, padding="same", filters=num_filters[0], kernel_size=filter_length),

    Conv1D(kernel_initializer=initializer, activation=activation, padding="same", filters=num_filters[1], kernel_size=filter_length),

    MaxPooling1D(pool_size=pool_length),

    Flatten(),

    Dense(units=num_hidden_nodes[0], kernel_initializer=initializer, activation=activation),

    Dense(units=num_hidden_nodes[1], kernel_initializer=initializer, activation=activation),

    Dense(units=3, activation="linear", input_dim=num_hidden_nodes[1]),
]) 

# compile model
loss_function = mean squared error
early_stopping_min_delta = 0.0001
early_stopping_patience = 4
reduce_lr_factor = 0.5
reuce_lr_epsilon = 0.0009
reduce_lr_patience = 2
reduce_lr_min = 0.00008

optimizer = Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=optimizer_epsilon, decay=0.0)

early_stopping = EarlyStopping(monitor='val_loss',     min_delta=early_stopping_min_delta, 
                                   patience=early_stopping_patience, verbose=2, mode='min')

reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.5, epsilon=reuce_lr_epsilon, 
                              patience=reduce_lr_patience,     min_lr=reduce_lr_min, mode='min', verbose=2)

model.compile(optimizer=optimizer, loss=loss_function)

model.fit(train_x, train_y, validation_data=(cv_x, cv_y),
      epochs=max_epochs, batch_size=batch_size, verbose=2,
      callbacks=[reduce_lr,early_stopping])

model.save('model_file.h5')

推荐答案

@frankyjuang将我链接到此处

@frankyjuang linked me to here

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

并将其与

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:

I have found a solution to both predicting using a tf graph and creating the jacobian function:

import tensorflow as tf
import numpy as np

# Create function to convert saved keras model to tensorflow graph
def 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

致电:

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:

Create function to load the tf model as a graph:

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

Create a function to make model predictions using the tf graph

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

做出预测:

tf_predictions = predict(tf_model_path,test_data)

Jacobian函数:

Jacobian function:

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)

这篇关于使用keras模型中的张量流图进行预测的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！