如何解决keras使用cov1D函数的输入问题-创新互联

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小编给大家分享一下如何解决keras使用cov1D函数的输入问题，希望大家阅读完这篇文章后大所收获，下面让我们一起去探讨方法吧！

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.h6', 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()

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