错误：不支持将字符串强制转换为float

编程问答 2022-06-03

问题描述

我是新来者，现在正在学习机器学习。我的第一个项目是带有fer2013的VGG 16。但是，当我运行代码时，此行始终存在“不支持浮空的字符串”的错误： model.fit（X_train，y_train，batch_size = 32，epochs = 30，verbose = 1，validation_data =（X_test，y_test）） 我尝试了几种将CSV文件的数据转换为float的方法，但是它们没有用。有人知道如何解决吗？ 这是我的完整代码：

import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers.core import Flatten,Dense,Dropout
from keras.layers.convolutional import Convolution2D,MaxPooling2D,ZeroPadding2D
from keras.optimizers import SGD
from keras.utils import np_utils
import cv2
import keras
import csv

emotion_data = pd.read_csv('/content/drive/My Drive/Emotion_Detection /fer2013.csv')
emotion_data.head()
emotion_data.info()

X_train = []
y_train = []
X_test = []
y_test = []
for index,row in emotion_data.iterrows():
    k = row['pixels'].split(" ")
    if row['Usage'] == 'Training':
        X_train.append(np.array(k))
        y_train.append(row['emotion'])
    elif row['Usage'] == 'PublicTest':
        X_test.append(np.array(k))
        y_test.append(row['emotion'])

X_train = np.array(X_train)
y_train = np.array(y_train)
X_test = np.array(X_test)
y_test = np.array(y_test)

X_train = X_train.reshape(X_train.shape[0],48,1)
X_test = X_test.reshape(X_test.shape[0],1)

y_train= np_utils.to_categorical(y_train,num_classes=7)
y_test = np_utils.to_categorical(y_test,num_classes=7)

model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape=(48,1)))
model.add(Convolution2D(64,3,activation='relu'))
model.add(ZeroPadding2D((1,activation='relu'))
model.add(MaxPooling2D((2,2),strides=(2,2)))

model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128,padding='same'))

model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256,1)))
model.add(Convolution2D(512,padding='same'))


model.add(ZeroPadding2D((1,padding='same'))

model.add(Flatten())
model.add(Dense(4096,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7,activation='softmax'))

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

model.fit(X_train,y_train,batch_size=32,epochs=30,verbose=1,validation_data=(X_test,y_test))

loss_and_metrics = model.evaluate(X_test,y_test)
print(loss_and_metrics)

解决方法

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