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keras-valueerror:无法将字符串转换为浮点

keras numpy csv pandas python

Simplicity · 技术社区 · 7 年前

代码如下所示,但出现以下错误:

valueerror:无法将字符串转换为float:braf

如果这是我的数据样本( | 只是我在这里添加的用于演示的分隔符,您可以想象CSV文件中单独单元格中的每个值):

C.401C>T皮肤23:141905805-141905805 9947布拉夫

可能是弦的问题吗?在这种情况下,如何读取和传递字符串?

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

1 回复 | 直到 7 年前

Milton Arango G 7 年前

如果csv(“braf”)的最后一个值是分类的,则可以使用keras 分类方法用一个热向量对其进行编码,这是神经网络的推荐编码方法。

from keras.utils import to_categorical

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

Encoding Categorical Features Keras Docs - to_categorical