Keras-ValueError:无法将字符串转换为浮点型

问题描述

我有下面显示的代码，但是出现以下错误:

I have the code shown below, but get the following error:

提供的这是我的数据示例( | 只是我在此处添加的分隔符，用于演示，您可以想象每个值都在CSV文件的单独的单元格中):

Provided that this is a sample of my data (| is just a separator I added here for demonstration, you can imagine each value in a separate cell in a CSV file):

可能是字符串引起的问题吗?在这种情况下，我该如何读取和传递字符串?

Could the strings be the issue? How can I read and pass strings in this case?

from keras.models import Sequential
from keras.layers import Dense
from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout, BatchNormalization, Activation
from keras.wrappers.scikit_learn import KerasRegressor

from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy as np

df1 = pd.read_csv('mutation-train.csv')
y = df1[['Histology']]
X = df1[["CDS_Mutation","Primary_Tissue","Genomic","Gene_ID","Official_Symbol"]]

X = X.astype(np.str).values
y = y.astype(np.str).values

df2 = pd.read_csv('mutation-test.csv')

X_Test = df2[["CDS_Mutation","Primary_Tissue","Genomic","Gene_ID","Official_Symbol"]]
X_Test = X_Test.astype(np.str).values

X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.2)

seed = 42
np.random.seed(seed)

model = Sequential()
#input layer
model.add(Dense(8, input_shape=(5,)))
model.add(BatchNormalization())
model.add(Activation("relu"))
model.add(Dropout(0.4))

model.add(Dense(8))
model.add(BatchNormalization())
model.add(Activation("sigmoid"))
model.add(Dropout(0.4))

model.add(Dense(4))
model.add(BatchNormalization())
model.add(Activation("sigmoid"))
model.add(Dropout(0.4))

model.add(Dense(2, activation="sigmoid"))

model.add(Dense(1, activation='linear'))

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

model.fit(X, y, nb_epoch=300, batch_size=30)

谢谢.

编辑

这是回溯:

File "my_code.py", line 16, in <module>
    df1 = pd.read_csv('mutation-train.csv',header=None,names=headers, dtype=dtypes)
  File "/Users/abder/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.py", line 678, in parser_f
    return _read(filepath_or_buffer, kwds)
  File "/Users/abder/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.py", line 446, in _read
    data = parser.read(nrows)
  File "/Users/abder/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.py", line 1036, in read
    ret = self._engine.read(nrows)
  File "/Users/abder/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.py", line 1848, in read
    data = self._reader.read(nrows)
  File "pandas/_libs/parsers.pyx", line 876, in pandas._libs.parsers.TextReader.read
  File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._read_low_memory
  File "pandas/_libs/parsers.pyx", line 968, in pandas._libs.parsers.TextReader._read_rows
  File "pandas/_libs/parsers.pyx", line 1094, in pandas._libs.parsers.TextReader._convert_column_data
  File "pandas/_libs/parsers.pyx", line 1162, in pandas._libs.parsers.TextReader._convert_tokens

推荐答案

如果csv('BRAF')的最后一个值是分类的，则可以使用keras to_categorical 方法使用以下方法对它进行编码:热向量，这是神经网络的推荐编码.

If the last value of your csv ('BRAF') is categorical you could use keras to_categorical method to encode it using one-hot vectors, this is the recommended encoding for neural networks.

from keras.utils import to_categorical

# one hot encode
encoded = to_categorical(data)
print(encoded)

编码分类特征 Keras文档-to_categorical

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