我读过这个例子https://github.com/fchollet/keras/blob/master/examples/mnist_mlp.py https://github.com/fchollet/keras/blob/master/examples/mnist_mlp.py并决定将这个想法应用到我的基础上,因为这是 Keras 最简单的神经网络。
这是我的基地https://drive.google.com/file/d/0B-B3QUQOzGZ7WVhzQmRsOTB0eFE/view https://drive.google.com/file/d/0B-B3QUQOzGZ7WVhzQmRsOTB0eFE/view(你可以下载我的csv文件,它只有83Kb)
This is picture my base:
基础形状 = (891, 23)
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop, Adam
import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
from keras.utils.vis_utils import model_to_dot
from IPython.display import SVG
from keras.utils import plot_model
base = pd.read_csv("mt.csv")
import pandas as pd
for col in base:
if col != "Fare" and col != "Age":
base[col]=base[col].astype(float)
X_train = base
y_train = base["Survived"]
del X_train["Survived"]
print("X_train=",X_train.shape)
print("y_train=", y_train.shape)
出去:
X_train= (891, 22)
y_train=(891,)
from sklearn.cross_validation import train_test_split
X_train, X_test , y_train, y_test = train_test_split(X_train, y_train, test_size=0.3, random_state=42)
batch_size = 4
num_classes = 2
epochs = 2
print(X_train.shape[1], 'train samples')
print(X_test.shape[1], 'test samples')
出去:
22 个训练样本
22个测试样品
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Dense(40, activation='relu', input_shape=(21,)))
model.add(Dropout(0.2))
#model.add(Dense(20, activation='relu'))
#odel.add(Dropout(0.2))
model.add(Dense(2, activation='sigmoid'))
model.summary()
Out:
层(类型)输出形状参数
密集_1(密集)(无,40)880
dropout_1(辍学)(无,40)0
密集_2(密集)(无,2)82
model.compile(loss='binary_crossentropy',
optimizer=Adam(),
metrics=['accuracy'])
plot_model(model, to_file='model.png')
SVG(model_to_dot(model).create(prog='dot', format='svg'))
print("X_train.shape=", X_train.shape)
print("X_test=",X_test.shape)
history = model.fit(X_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(X_test, y_test))
回溯(最近一次调用最后一次):
文件“new.py”,第 67 行,位于
验证数据=(X_测试,y_测试))
文件“miniconda3/lib/python3.6/site-packages/keras/models.py”,第 845 行,适合
初始纪元=初始纪元)
文件“miniconda3/lib/python3.6/site-packages/keras/engine/training.py”,第 1405 行,适合
批量大小=批量大小)
文件“miniconda3/lib/python3.6/site-packages/keras/engine/training.py”,第 1295 行,位于 _standardize_user_data
exception_prefix='模型输入')
文件“miniconda3/lib/python3.6/site-packages/keras/engine/training.py”,第 133 行,位于 _standardize_input_data
str(数组.形状))
ValueError:检查模型输入时出错:预期密集_1_输入具有形状(无,21),但得到形状为(623,22)的数组
[在 5.1 秒内完成,退出代码为 1]
我该如何解决这个错误?我尝试更改输入形状,例如更改为 (20,) 或 (22,) 等,但没有成功。
例如,如果 input_shape=(22,) 我有错误: File "miniconda3/lib/python3.6/site-packages/pandas/core/indexing.py", line 1873, in Maybe_convert_indices
raise IndexError("索引超出范围")