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解决keras使用cov1D函数的输入问题代码解析
时间:2020-06-29 编辑:袖梨 来源:一聚教程网
本篇文章小编给大家分享一下解决keras使用cov1D函数的输入问题代码解析,代码介绍的很详细,小编觉得挺不错的,现在分享给大家供大家参考,有需要的小伙伴们可以来看看。
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()
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