Question

我训练了下面的网并保存了它。编译重新加载的网络时，会出现错误：

ValueError: Error when checkingModelTarget: expected dense_3 to haveFast (None, 1) but got array with shape (10000, 10)

可能是什么原因？许多类似问题的解决方案对我没有帮助。

代码：

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Convolution2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
from keras.models import model_from_json

K.set_image_dim_ordering('th')

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)

# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshape to be [samples][pixels][width][height]
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32')

# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]

def larger_model():
    # create model
    model = Sequential()
    model.add(Convolution2D(30, 5, 5, border_mode='valid', input_shape=(1, 28, 28), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(15, 3, 3, activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(50, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

# build the model
model = larger_model()
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=1, batch_size=200, verbose=2)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))


# save model and weights
print("Saving model...")
model_json = model.to_json()
with open('mnist_model.json', 'w') as json_file:
    json_file.write(model_json)
model.save_weights("mnist_weights.h5")
print("model saved to disk")

# load model and weights
print("Laoding model...")
with open('mnist_model.json') as json_file:
    model_json = json_file.read()

model = model_from_json(model_json)
model.load_weights('mnist_weights.h5')
print("mode loaded from disk")

print("compiling model...")
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))

Answer 1

为什么在加载模型后执行此操作？：

model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

您的基本模型使用categorical_crossentropy，区别在于最新预期分类，一个热编码目标，稀疏版本需要索引并在后台调用np.utils.to_categorical()。所以在这里，keras抱怨，因为你使用稀疏版本，它期望索引如此形状(?, 1)但你喂y_test，编码为一个热的形状(?, 10)。

解决方案，要么不改变损失的类型并使用：

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

加载模型后，或反转一个热编码y_test：

y_test = np.argmax(y_test)

我希望这会有所帮助： - ）

编译加载的模型时的Keras ValueError

1 个答案: