[英]Error making prediction with Keras model
我正在嘗試使用預先訓練的Keras模型對樣本進行預測,但出現錯誤。 我已經詳細介紹了模型訓練腳本的各個部分,以顯示數據准備,矩陣形狀和模型規格。
矩陣形狀和數據准備:
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)
型號規格:
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'])
在完全獨立的程序中,重新加載了預訓練的模型,對輸入樣本矩陣進行了整形,以匹配模型期望的結果,並對數據應用相同的歸一化。 像這樣
預測方法:
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))
當調用get_prediction時,將給出此輸出;
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.
但是嘗試進行預測時出現此錯誤;
Exception: ValueError: Tensor Tensor("activation_4/Softmax:0", shape=(?, 3), dtype=float32) is not an element of this graph.
我很沮喪 誰能指出這里有什么問題以及如何糾正它? 非常感謝。
注意所有的訓練和預測都在使用Python 3和Keras 2.1.3和Tensorflow 1.5.0的Windows 10計算機上進行的
考慮到這個github問題就得出了答案。 在這種情況下, get_prediction()
將由與加載模型的線程不同的線程調用。 進行以下更改可清除錯誤:
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))
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