machine-learning - 检查输入时如何解决错误:大小不正确
问题描述
这是我第一次尝试解决 kaggle 上的任务。这是任务页面 - https://www.kaggle.com/c/bike-sharing-demand。我写了这段代码(我有一些多余的代码行,因为我不确定我现在需要什么):
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import keras
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
train_data = pd.read_csv('/kaggle/input/bike-sharing-demand/train.csv')
train_targets = train_data[['casual', 'registered', 'count']]
train_datetime_helper = train_data[['datetime']]
dt = pd.DatetimeIndex(train_data['datetime'])
train_data['day'] = dt.day
train_data['month'] = dt.month
train_data['year'] = dt.year
train_data['hour'] = dt.hour
train_data['dow'] = dt.dayofweek
train_data['woy'] = dt.weekofyear
train_data = train_data.drop(['casual', 'registered', 'count', 'datetime'], axis=1)
test_data = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')
test_datetime_helper = test_data[['datetime']]
dt = pd.DatetimeIndex(test_data['datetime'])
test_data['day'] = dt.day
test_data['month'] = dt.month
test_data['year'] = dt.year
test_data['hour'] = dt.hour
test_data['dow'] = dt.dayofweek
test_data['woy'] = dt.weekofyear
test_data = test_data.drop(['datetime'], axis=1)
mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std
test_data -= mean
test_data /= std
from keras import models
from keras import layers
from keras.layers import Dense, Conv2D, Flatten
def build_model():
model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(train_data.shape[1], train_targets.shape[1])))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(1))
model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
return model
k = 4
num_val_samples = len(train_data) // k
num_epochs = 100
all_scores = []
for i in range(k):
print('Processing fold #', i)
val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]
val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]
partial_train_data = np.concatenate(
[train_data[:i * num_val_samples],
train_data[(i + 1) * num_val_samples:]], axis=0)
partial_train_targets = np.concatenate(
[train_targets[:i * num_val_samples],
train_targets[(i + 1) * num_val_samples:]], axis=0)
model = build_model()
model.fit(partial_train_data, partial_train_targets,
epochs=num_epochs, batch_size=1, verbose=0)
val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)
all_scores.append(val_mae)
我有这个错误。你能解释一下我该如何解决吗在此处输入图像描述
解决方案
您指定了错误的模型输入尺寸。尝试以这种方式定义您的第一层
model.add(layers.Dense(64, activation='relu', input_shape=(train_data.shape[1],)))
推荐阅读
- pip - pip install 找不到 pip search 公布的最新版本
- typescript - 打字稿泛型错误地处理部分
- c# - .Net Core 3 EF - 订单包含数组
- c# - TypeInitializationException Stripe.StripeConfiguration 支付
- java - @PostConstruct 和@PreDestroy 注解的实时应用使用
- wordpress - 在不调用 setAttributes 的情况下重新渲染 Gutenberg 块?
- components - 移位 BCD 位时的 VHDL 问题
- java - 即使使用 dp,布局也不适合屏幕截图
- ibm-mobilefirst - IBM 移动迁移:Worklight 6.2 到 8.0
- python - 如何在 Django 上做日志系统?