我已经阅读了这个例子https://github.com/fchollet/keras/blob/master/examples/mnist_mlp.py并决定将这个想法用于我的基础，因为这是Keras最简单的NN .

这是我的基础https://drive.google.com/file/d/0B-B3QUQOzGZ7WVhzQmRsOTB0eFE/view（你可以下载我的csv文件，它只有83Kb）

这是我的基础图片：

base.shape =（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：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：22列车样品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()

日期：

图层（类型）输出形状参数

dense_1（密集）（无，40）880

dropout_1（Dropout）（无，40）0

dense_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，在validation_data =（X_test，y_test））

文件“miniconda3 / lib / python3.6 / site-packages / keras / models.py”，第845行，符合initial_epoch = initial_epoch）

文件“miniconda3 / lib / python3.6 / site-packages / keras / engine / training.py”，第1405行，适合batch_size = batch_size）

文件“miniconda3 / lib / python3.6 / site-packages / keras / engine / training.py”，第1295行，_standardize_user_data exception_prefix ='model input'）

在_standardize_input_data str（array.shape）中输入文件“miniconda3 / lib / python3.6 / site-packages / keras / engine / training.py”，第133行

ValueError：检查模型输入时出错：期望的dense_1_input具有形状（无，21）但是具有形状的数组（623,22）[在5.1s中完成，退出代码为1]

How can I solve this error? I tried to change input shape, for example, to (20,) or to (22,), etc. Without success.

例如，如果input_shape =（22，）我有错误：文件“miniconda3 / lib / python3.6 / site-packages / pandas / core / indexing.py”，第1873行，在maybe_convert_indices中引发IndexError（“索引已经出来” -of界“）