解决了以下错误：
1.ValueError: Input 0 is incompatible with layer conv1d_1: expected ndim=3, found ndim=4
2.ValueError: Error when checking target: expected dense_3 to have 3 dimensions, but got array with …
1.ValueError: Input 0 is incompatible with layer conv1d_1: expected ndim=3, found ndim=4
错误代码：
model.add(Conv1D(8, kernel_size=3, strides=1, padding='same', input_shape=(x_train.shape))
或者
model.add(Conv1D(8, kernel_size=3, strides=1, padding='same', input_shape=(x_train.shape[1:])))
这是因为模型输入的维数有误，在使用基于tensorflow的keras中，cov1d的input_shape是二维的，应该：
1、reshape x_train的形状
x_train=x_train.reshape((x_train.shape[0],x_train.shape[1],1))
x_test = x_test.reshape((x_test.shape[0], x_test.shape[1],1))
2、改变input_shape
model = Sequential()
model.add(Conv1D(8, kernel_size=3, strides=1, padding='same', input_shape=(x_train.shape[1],1)))
大神原文：
The input shape is wrong, it should be input_shape = (1, 3253) for Theano or (3253, 1) for TensorFlow. The input shape doesn't include the number of samples.
Then you need to reshape your data to include the channels axis:
x_train = x_train.reshape((500000, 1, 3253))
Or move the channels dimension to the end if you use TensorFlow. After these changes it should work.
2.ValueError: Error when checking target: expected dense_3 to have 3 dimensions, but got array with …
出现此问题是因为ylabel的维数与x_train x_test不符，既然将x_train x_test都reshape了，那么也需要对y进行reshape。
解决办法：
同时对照x_train改变ylabel的形状
t_train=t_train.reshape((t_train.shape[0],1))
t_test = t_test.reshape((t_test.shape[0],1))
附：
修改完的代码：
import warnings
warnings.filterwarnings("ignore")
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import pandas as pd
import numpy as np
import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from keras.models import Sequential
from keras.layers import Dense, Dropout, BatchNormalization, Activation, Flatten, Conv1D
from keras.callbacks import LearningRateScheduler, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras import optimizers
from keras.regularizers import l2
from keras.models import load_model
df_train = pd.read_csv('./input/train_V2.csv')
df_test = pd.read_csv('./input/test_V2.csv')
df_train.drop(df_train.index[[2744604]],inplace=True)#去掉nan值
df_train["distance"] = df_train["rideDistance"]+df_train["walkDistance"]+df_train["swimDistance"]
# df_train["healthpack"] = df_train["boosts"] + df_train["heals"]
df_train["skill"] = df_train["headshotKills"]+df_train["roadKills"]
df_test["distance"] = df_test["rideDistance"]+df_test["walkDistance"]+df_test["swimDistance"]
# df_test["healthpack"] = df_test["boosts"] + df_test["heals"]
df_test["skill"] = df_test["headshotKills"]+df_test["roadKills"]
df_train_size = df_train.groupby(['matchId','groupId']).size().reset_index(name='group_size')
df_test_size = df_test.groupby(['matchId','groupId']).size().reset_index(name='group_size')
df_train_mean = df_train.groupby(['matchId','groupId']).mean().reset_index()
df_test_mean = df_test.groupby(['matchId','groupId']).mean().reset_index()
df_train = pd.merge(df_train, df_train_mean, suffixes=["", "_mean"], how='left', on=['matchId', 'groupId'])
df_test = pd.merge(df_test, df_test_mean, suffixes=["", "_mean"], how='left', on=['matchId', 'groupId'])
del df_train_mean
del df_test_mean
df_train = pd.merge(df_train, df_train_size, how='left', on=['matchId', 'groupId'])
df_test = pd.merge(df_test, df_test_size, how='left', on=['matchId', 'groupId'])
del df_train_size
del df_test_size
target = 'winPlacePerc'
train_columns = list(df_test.columns)
""" remove some columns """
train_columns.remove("Id")
train_columns.remove("matchId")
train_columns.remove("groupId")
train_columns_new = []
for name in train_columns:
if '_' in name:
train_columns_new.append(name)
train_columns = train_columns_new
# print(train_columns)
X = df_train[train_columns]
Y = df_test[train_columns]
T = df_train[target]
del df_train
x_train, x_test, t_train, t_test = train_test_split(X, T, test_size = 0.2, random_state = 1234)
# scaler = preprocessing.MinMaxScaler(feature_range=(-1, 1)).fit(x_train)
scaler = preprocessing.QuantileTransformer().fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)
Y = scaler.transform(Y)
x_train=x_train.reshape((x_train.shape[0],x_train.shape[1],1))
x_test = x_test.reshape((x_test.shape[0], x_test.shape[1],1))
t_train=t_train.reshape((t_train.shape[0],1))
t_test = t_test.reshape((t_test.shape[0],1))
model = Sequential()
model.add(Conv1D(8, kernel_size=3, strides=1, padding='same', input_shape=(x_train.shape[1],1)))
model.add(BatchNormalization())
model.add(Conv1D(8, kernel_size=3, strides=1, padding='same'))
model.add(Conv1D(16, kernel_size=3, strides=1, padding='valid'))
model.add(BatchNormalization())
model.add(Conv1D(16, kernel_size=3, strides=1, padding='same'))
model.add(Conv1D(32, kernel_size=3, strides=1, padding='valid'))
model.add(BatchNormalization())
model.add(Conv1D(32, kernel_size=3, strides=1, padding='same'))
model.add(Conv1D(32, kernel_size=3, strides=1, padding='same'))
model.add(Conv1D(64, kernel_size=3, strides=1, padding='same'))
model.add(Activation('tanh'))
model.add(Flatten())
model.add(Dropout(0.5))
# model.add(Dropout(0.25))
model.add(Dense(512,kernel_initializer='he_normal', activation='relu', W_regularizer=l2(0.01)))
model.add(Dense(128,kernel_initializer='he_normal', activation='relu', W_regularizer=l2(0.01)))
model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))
optimizers.Adam(lr=0.01, epsilon=1e-8, decay=1e-4)
model.compile(optimizer=optimizer, loss='mse', metrics=['mae'])
model.summary()
ng = EarlyStopping(monitor='val_mean_absolute_error', mode='min', patience=4, verbose=1)
# model_checkpoint = ModelCheckpoint(filepath='best_model.h5', monitor='val_mean_absolute_error', mode = 'min', save_best_only=True, verbose=1)
# reduce_lr = ReduceLROnPlateau(monitor='val_mean_absolute_error', mode = 'min',factor=0.5, patience=3, min_lr=0.0001, verbose=1)
history = model.fit(x_train, t_train,
validation_data=(x_test, t_test),
epochs=30,
batch_size=32768,
callbacks=[early_stopping],
verbose=1)predict(Y)
pred = pred.ravel()
补充知识：Keras Conv1d 参数及输入输出详解
Conv1d(in_channels，out_channels，kernel_size，stride=1，padding=0，dilation=1，groups=1，bias=True)
filters:卷积核的数目(即输出的维度)
kernel_size: 整数或由单个整数构成的list/tuple，卷积核的空域或时域窗长度
strides: 整数或由单个整数构成的list/tuple，为卷积的步长。任何不为1的strides均为任何不为1的dilation_rata均不兼容
padding: 补0策略，为”valid”，”same”或”casual”，”casual”将产生因果(膨胀的)卷积，即output[t]不依赖于input[t+1:]。当对不能违反事件顺序的时序信号建模时有用。“valid”代表只进行有效的卷积，即对边界数据不处理。“same”代表保留边界处的卷积结果，通常会导致输出shape与输入shape相同。
activation:激活函数，为预定义的激活函数名,或逐元素的Theano函数。如果不指定该函数，将不会使用任何激活函数(即使用线性激活函数:a(x)=x)
model.add(Conv1D(filters=nn_params["input_filters"],
kernel_size=nn_params["filter_length"],
strides=1,
padding='valid',
activation=nn_params["activation"],
kernel_regularizer=l2(nn_params["reg"])))
例：输入维度为（None，1000，4）
第一维度：None
第二维度：
output_length = int((input_length - nn_params["filter_length"] + 1))
在此情况下为：
output_length = （1000 + 2*padding - filters +1）/ strides = （1000 + 2*0 -32 +1）/1 = 969
第三维度：filters
以上这篇解决keras使用cov1D函数的输入问题就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。
