Question

我试图使用预先训练过的Keras模型对样本进行预测，但是我收到了错误。我是模型训练脚本的详细部分，用于显示数据准备，矩阵形状和模型规范;

Matrix Shapes＆amp;数据准备：

from __future__ import print_function
#import numpy as np
np.random.seed(1337)  # for reproducibility

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K

batchsize = 128
nb_classes = 3
nb_epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
pool_size = (2, 2)
# convolution kernel size
kernel_size = (3, 3)

# the data, shuffled and split between train and test sets
#(X_train, y_train), (X_test, y_test) = mnist.load_data()

if K.image_dim_ordering() == 'th':
    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

型号规格：

model = Sequential()

model.add(Convolution2D(nb_filters, [kernel_size[0], kernel_size[1]],
                        padding='valid',
                        input_shape=input_shape,
                        name='conv2d_1'))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, [kernel_size[0], kernel_size[1]], name='conv2d_2'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=pool_size, name='maxpool2d'))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128, name='dense_1'))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes, name='dense_2'))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='adadelta',
              metrics=['accuracy'])

在完全独立的程序中，重新加载预训练的模型，重新输入输入样本矩阵以匹配模型所期望的和应用于数据的相同标准化。像这样;

预测方法：

from keras import backend as K
from keras.models import load_model

img_rows, img_cols = 28, 28

#Load the pre-trained classifier model
retrieved_model = load_model('classifier_cnn_saved_model_0.05_30min.hdf5')

#Function to callback
def get_prediction(sample):
    print('Received: ' + str(sample.shape))
    if K.image_dim_ordering() == 'th':
        sample = sample.reshape(sample.shape[0], 1, img_rows, img_cols)
    else:
        sample = sample.reshape(sample.shape[0], img_rows, img_cols, 1)

    print('Reshaped for backend: ' + K.image_dim_ordering() + ' ' + str(sample.shape))
    sample = sample.astype('float32')   
    sample /= 255 #normalize the sample data
    prediction = retrieved_model.predict(sample)
    print('pyAgent; ' + str(sample.shape) + ' prediction: ' + str(prediction))

在调用get_prediction时会给出此输出;

Received: (1, 784) <====== Yep, as expected.
Reshaped for backend: tf (1, 28, 28, 1) <====== What the model expects, I think. Based on how it was specified at training time.

但是在尝试预测时会出现此错误;

Exception: ValueError: Tensor Tensor("activation_4/Softmax:0", shape=(?, 3), dtype=float32) is not an element of this graph.

我很难过。任何人都可以请指出这里有什么错误以及如何纠正它？非常感谢。

N.B。使用带有Keras 2.1.3和Tensorflow 1.5.0的Python 3在同一台Windows 10机器上进行所有培训和预测

Answer 1

考虑到这一点github issue得出了答案。在这种情况下，get_prediction()将由与加载模型的线程不同的线程调用。进行这些更改可以清除错误：

import tensorflow as tf #<======= add this
from keras import backend as K
from keras.models import load_model

img_rows, img_cols = 28, 28

#Load the pre-trained classifier model
retrieved_model = load_model('classifier_cnn_saved_model_0.05_30min.hdf5')
#https://www.tensorflow.org/api_docs/python/tf/Graph
graph = tf.get_default_graph() #<======= do this right after constructing or loading the model

#Function to callback
def get_prediction(sample):
    print('Received: ' + str(sample.shape))
    if K.image_dim_ordering() == 'th':
        sample = sample.reshape(sample.shape[0], 1, img_rows, img_cols)
    else:
        sample = sample.reshape(sample.shape[0], img_rows, img_cols, 1)

    print('Reshaped for backend: ' + K.image_dim_ordering() + ' ' + str(sample.shape))
    sample = sample.astype('float32')   
    sample /= 255 #normalize the sample data
    with graph.as_default(): #<======= with this, call predict
        prediction = retrieved_model.predict_classes(sample)
    print('pyAgent; ' + str(sample.shape) + ' prediction: ' + str(prediction))

使用Keras模型进行预测时出错

1 个答案: