下面是我的代码:
import tensorflow as tf
import numpy as np
import pandas as pd
import functools
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense
from tensorflow.python.keras.losses import binary_crossentropy
def process_continuous_data(mean, data):
# Center the data
data = tf.cast(data, tf.float32) * 1/(2*mean)
return tf.reshape(data, [-1, 1])
# Load training data
path = 'train_data.csv'
data_train_tf=pd.read_csv(path)
col=["Index","WeightKg","Age","Speed","V1_LF","V1_RF","V1_LH","V1_RH","STD_V1_LF","STD_V1_RF","STD_V1_LH","STD_V1_RH","V2_LF","V2_RF","V2_LH","V2_RH","STD_V2_LF","STD_V2_RF","STD_V2_LH","STD_V2_RH","V3_LF","V3_RF","V3_LH","V3_RH","STD_V3_LF","STD_V3_RF","STD_V3_LH","STD_V3_RH","V4_LF","V4_RF","V4_LH","V4_RH","STD_V4_LF","STD_V4_RF","STD_V4_LH","STD_V4_RH","V5_LF","V5_RF","V5_LH","V5_RH","STD_V5_LF","STD_V5_RF","STD_V5_LH","STD_V5_RH","V6_LF","V6_RF","V6_LH","V6Z_RH","STD_V6_LF","STD_V6_RF","STD_V6_LH","STD_V6_RH","V7_LF","V7_RF","V7_LH","V7_RH","STD_V7_LF","STD_V7_RF","STD_V7_LH","STD_V7_RH","V8_LF","V8_RF","V8_LH","V8_RH","STD_V8_LF","STD_V8_RF","STD_V8_LH","STD_V8_RH","V9_LF","V9_RF","V9_LH","V9_RH","STD_V9_LF","STD_V9_RF","STD_V9_LH","STD_V9_RH","V10_LF","V10_RF","V10_LH","V10_RH","STD_V10_LF","STD_V10_RF","STD_V10_LH","STD_V10_RH","V11_LF","V11_RF","V11_LH","V11_RH","STD_V11_LF","STD_V11_RF","STD_V11_LH","STD_V11_RH","V12_LF","V12_RF","V12_LH","V12_RH","STD_V12_LF","STD_V12_RF","STD_V12_LH","STD_V12_RH","V13_LF","V13_RF","V13_LH","V13_RH","STD_V13_LF","STD_V13_RF","STD_V13_LH","STD_V13_RH","Mean_V14_LF","Mean_V14_RF","Mean_V14_LH","Mean_V14_RH","STD_V14_LF","STD_V14_RF","STD_V14_LH","STD_V14_RH","V15_LF","V15_RF","V15_LH","V15_RH","STD_V15_LF","STD_V15_RF","STD_V15_LH","STD_V15_RH","V16_LF","V16_RF","V16_LH","V16_RH","V17_LF","V17_RF","V17_LH","V17_RH","V18_LF","V18_RF","V18_LH","V18_RH","V19_LF","V19_RF","V19_LH","V19_RH","V20_LF","V20_RF","V20_LH","V20_RH","V21_LF","V21_RF","V21_LH","V21_RH","V22_LF","V22_RF","V22_LH","V22_RH","V23_LF","V23_RF","V23_LH","V23_RH","V24_LF","V24_RF","V24_LH","V24_RH","V25_LF","V25_RF","V25_LH","V25_RH","V26_LF","V26_RF","V26_LH","V26_RH","V27_LF","V27_RF","V27_LH","V27_RH","V28_LF","V28_RF","V28_LH","V28_RH","V29_LF","V29_RF","V29_LH","V29_RH","V30_LF","V30_RF","V30_LH","V30_RH","Speed_trot","V1_LF_trot","V1_RF_trot","V1_LH_trot","V1_RH_trot","STD_V1_LF_trot","STD_V1_RF_trot","STD_V1_LH_trot","STD_V1_RH_trot","V2_LF_trot","V2_RF_trot","V2_LH_trot","V2_RH_trot","STD_V2_LF_trot","STD_V2_RF_trot","STD_V2_LH_trot","STD_V2_RH_trot","V3_LF_trot","V3_RF_trot","V3_LH_trot","V3_RH_trot","STD_V3_LF_trot","STD_V3_RF_trot","STD_V3_LH_trot","STD_V3_RH_trot","V4_LF_trot","V4_RF_trot","V4_LH_trot","V4_RH_trot","STD_V4_LF_trot","STD_V4_RF_trot","STD_V4_LH_trot","STD_V4_RH_trot","V5_LF_trot","V5_RF_trot","V5_LH_trot","V5_RH_trot","STD_V5_LF_trot","STD_V5_RF_trot","STD_V5_LH_trot","STD_V5_RH_trot","V6_LF_trot","V6_RF_trot","V6_LH_trot","V6Z_RH_trot","STD_V6_LF_trot","STD_V6_RF_trot","STD_V6_LH_trot","STD_V6_RH_trot","V7_LF_trot","V7_RF_trot","V7_LH_trot","V7_RH_trot","STD_V7_LF_trot","STD_V7_RF_trot","STD_V7_LH_trot","STD_V7_RH_trot","V8_LF_trot","V8_RF_trot","V8_LH_trot","V8_RH_trot","STD_V8_LF_trot","STD_V8_RF_trot","STD_V8_LH_trot","STD_V8_RH_trot","V9_LF_trot","V9_RF_trot","V9_LH_trot","V9_RH_trot","STD_V9_LF_trot","STD_V9_RF_trot","STD_V9_LH_trot","STD_V9_RH_trot","V10_LF_trot","V10_RF_trot","V10_LH_trot","V10_RH_trot","STD_V10_LF_trot","STD_V10_RF_trot","STD_V10_LH_trot","STD_V10_RH_trot","V11_LF_trot","V11_RF_trot","V11_LH_trot","V11_RH_trot","STD_V11_LF_trot","STD_V11_RF_trot","STD_V11_LH_trot","STD_V11_RH_trot","V12_LF_trot","V12_RF_trot","V12_LH_trot","V12_RH_trot","STD_V12_LF_trot","STD_V12_RF_trot","STD_V12_LH_trot","STD_V12_RH_trot","V13_LF_trot","V13_RF_trot","V13_LH_trot","V13_RH_trot","STD_V13_LF_trot","STD_V13_RF_trot","STD_V13_LH_trot","STD_V13_RH_trot","Mean_V14_LF_trot","Mean_V14_RF_trot","Mean_V14_LH_trot","Mean_V14_RH_trot","STD_V14_LF_trot","STD_V14_RF_trot","STD_V14_LH_trot","STD_V14_RH_trot","V15_LF_trot","V15_RF_trot","V15_LH_trot","V15_RH_trot","STD_V15_LF_trot","STD_V15_RF_trot","STD_V15_LH_trot","STD_V15_RH_trot","V16_LF_trot","V16_RF_trot","V16_LH_trot","V16_RH_trot","V17_LF_trot","V17_RF_trot","V17_LH_trot","V17_RH_trot","V18_LF_trot","V18_RF_trot","V18_LH_trot","V18_RH_trot","V19_LF_trot","V19_RF_trot","V19_LH_trot","V19_RH_trot","V20_LF_trot","V20_RF_trot","V20_LH_trot","V20_RH_trot","V21_LF_trot","V21_RF_trot","V21_LH_trot","V21_RH_trot","V22_LF_trot","V22_RF_trot","V22_LH_trot","V22_RH_trot","V23_LF_trot","V23_RF_trot","V23_LH_trot","V23_RH_trot","V24_LF_trot","V24_RF_trot","V24_LH_trot","V24_RH_trot","V25_LF_trot","V25_RF_trot","V25_LH_trot","V25_RH_trot","V26_LF_trot","V26_RF_trot","V26_LH_trot","V26_RH_trot","V27_LF_trot","V27_RF_trot","V27_LH_trot","V27_RH_trot","V28_LF_trot","V28_RF_trot","V28_LH_trot","V28_RH_trot","V29_LF_trot","V29_RF_trot","V29_LH_trot","V29_RH_trot","V30_LF_trot","V30_RF_trot","V30_LH_trot","V30_RH_trot","Group0-NormalControl1Affected"]
feature_names = col[:-1]
label_name = col[-1]
dataset = tf.data.experimental.make_csv_dataset("train_data.csv", batch_size=32 ,column_names=col,label_name=label_name)
test_dataset = tf.data.experimental.make_csv_dataset("test_data_s.csv", batch_size=32 ,column_names=feature_names)
desc = pd.read_csv('train_data.csv')[feature_names].describe()
MEAN = np.array(desc.T['mean'])
numerical_columns = []
for feature_id in range(len(feature_names)):
num_col = tf.feature_column.numeric_column(feature_names[feature_id], normalizer_fn=functools.partial(process_continuous_data, MEAN[feature_id]))
numerical_columns.append(num_col)
# create a feature layer that will transform the input data
numeric_layer = tf.keras.layers.DenseFeatures(numerical_columns)
# Create model
model=Sequential()
model.add(numeric_layer)
model.add(Dense(20, activation='relu'))
model.add(Dense(12, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss=binary_crossentropy, optimizer='adam', metrics=['accuracy'])
model.fit(dataset, epochs=10, steps_per_epoch=100)
# # Predict
predictions = model.predict(test_dataset)
print(predictions)
# #save predict result
np.savetxt('result.csv', predictions, delimiter = ',')
当我训练模型时没有错误或异常，但是当我运行这一行时:预测= model.predict(test_data)
它总是说"不支持将字符串强制转换为float ">
我的测试数据如下所示:
(有365个功能，我在这里展示三个)
Index WeightKg Age
143 38.3 1.56
154 23.9 2.24
30 25.1 4.01
111 38.8 5.49
183 36.5 3.21
预测应该是每行0或1之间的单个值所以我不知道这个字符串是从哪里来的我用熊猫来查看字体，所有的功能都是float64谁能告诉我我哪里做错了?第一次做这样的事
问题解决了。这是因为在测试CSV中有一些空白单元格。因此，TensorFlow将它们视为字符串。