为什么 SparseCategoricalCrossentropy 不适用于此机器学习模型？
Why SparseCategoricalCrossentropy is not working with this machine learning model?
我有一个 .csv 数据库文件，如下所示：
Day Hour N1 N2 N3 N4 N5 ... N14 N15 N16 N17 N18 N19 N20
0 1996-03-18 15:00 4 9 10 16 21 ... 48 62 66 68 73 76 78
1 1996-03-19 15:00 6 12 15 19 28 ... 63 64 67 69 71 75 77
2 1996-03-21 15:00 2 4 6 7 15 ... 52 54 69 72 73 75 77
3 1996-03-22 15:00 3 8 15 17 19 ... 49 60 61 64 67 68 75
4 1996-03-25 15:00 2 10 11 14 18 ... 55 60 61 66 67 75 79
... ... ... .. .. .. .. .. ... ... ... ... ... ... ... ...
13596 2022-01-04 22:50 17 18 22 26 27 ... 64 65 71 72 73 76 80
13597 2022-01-05 15:00 1 5 8 14 15 ... 47 54 59 67 70 72 76
13598 2022-01-05 22:50 6 7 14 15 16 ... 54 55 59 61 70 71 80
13599 2022-01-06 15:00 9 10 11 17 28 ... 51 55 65 67 72 76 78
13600 2022-01-06 22:50 1 2 6 9 11 ... 51 52 54 67 68 73 75
但我正在尝试通过在最后一层使用 softmax 函数和 SparseCategoricalCrossentropy()[=33= 来开发该 1D CNN 模型的修改版本] 作为损失函数，并通过向该代码添加新函数使其与众不同。
这是我目前的代码以及我正在尝试构建和使用的模型：
# multivariate output 1d cnn example
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # or any {'0', '1', '2'}
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
# multivariate output 1d cnn example
from numpy import array
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import *
from tensorflow.keras.losses import *
from tensorflow.keras.layers import *
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.callbacks import ReduceLROnPlateau
from tensorflow.keras.callbacks import ModelCheckpoint
# Define the Required Callback Function
class printlearningrate(tf.keras.callbacks.Callback):
def on_epoch_end (self, epoch, logs={}):
optimizer = self.model.optimizer
lr = K.eval(optimizer.lr)
Epoch_count = epoch + 1
print('\n', "Epoch:", Epoch_count, ', Learning Rate: {:.7f}'.format(lr))
printlr = printlearningrate()
# split a multivariate sequence into samples
def split_sequences (sequences, n_steps):
X, y = list(), list()
for i in range(len(sequences)):
# find the end of this pattern
end_ix = i + n_steps
# check if we are beyond the dataset
if end_ix > len(sequences) - 1:
break
# gather input and output parts of the pattern
seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
df = pd.read_csv('DrawsDB.csv')
print(df)
# df['Time'] = df[['Day', 'Hour']].agg(' '.join, axis=1)
df.insert(0, 'Time', df[['Day', 'Hour']].agg(' '.join, axis=1))
df.drop(columns=['Day', 'Hour'], inplace=True)
df.set_index('Time', inplace=True)
print(df)
numpy_array = df.to_numpy()
print(type(numpy_array))
print(numpy_array)
# choose a number of time steps
n_steps = 10
# convert into input/output
X, y = split_sequences(numpy_array, n_steps)
print(X.shape, y.shape)
# the dataset knows the number of features, e.g. 2
n_features = X.shape[2]
# Reduce learning rate when nothing happens to lower more the loss:
reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.9888888888888889,
patience=10, min_lr=0.0000001, verbose=1)
epochs = 10
# saving best model every epoch with ModelCheckpoint:
checkpoint_filepath = 'C:\Path\To\Saved\CheckPoint\model\'
model_checkpoint_callback = ModelCheckpoint(
filepath=checkpoint_filepath,
monitor='loss',
save_best_only=True,
save_weights_only=True,
verbose=1)
# define model
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=2, activation=LeakyReLU(), input_shape=(n_steps, n_features)))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(50, activation=LeakyReLU()))
model.add(Dense(n_features))
model.compile(optimizer=Nadam(lr=0.09), loss=SparseCategoricalCrossentropy(),
metrics=['accuracy', mean_squared_error, mean_absolute_error, mean_absolute_percentage_error])
# fit model
model.fit(X, y, epochs=10, verbose=2, callbacks=[printlr, reduce_lr, model_checkpoint_callback])
split_sequences 函数就像它的名字说的那样，它通过仅从数据库中获取 N 行作为输入并尝试从中预测所有 N+1 行来拆分数据库数据库作为输出。
但是，我认为存在问题，因为每次我尝试 运行 python 脚本时都会收到此错误：
tensorflow.python.framework.errors_impl.InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [32,20] and labels shape [640]
[[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits
(defined at C:\Users\UserName\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\backend.py:5114)
]] [Op:__inference_train_function_1228]
请问您知道如何解决这个问题吗？
提前致谢！
假设标签是整数，它们的形状不适合 SparseCategoricalCrossentropy。检查 docs。
尝试将您的 y 转换为 one-hot 编码标签：
y = tf.keras.utils.to_categorical(y, num_classes=20)
并将损失函数更改为 CategoricalCrossentropy:
model.compile(optimizer=Nadam(lr=0.09), loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=['accuracy', mean_squared_error, mean_absolute_error, mean_absolute_percentage_error])
它应该可以工作。
我有一个 .csv 数据库文件，如下所示：
Day Hour N1 N2 N3 N4 N5 ... N14 N15 N16 N17 N18 N19 N20
0 1996-03-18 15:00 4 9 10 16 21 ... 48 62 66 68 73 76 78
1 1996-03-19 15:00 6 12 15 19 28 ... 63 64 67 69 71 75 77
2 1996-03-21 15:00 2 4 6 7 15 ... 52 54 69 72 73 75 77
3 1996-03-22 15:00 3 8 15 17 19 ... 49 60 61 64 67 68 75
4 1996-03-25 15:00 2 10 11 14 18 ... 55 60 61 66 67 75 79
... ... ... .. .. .. .. .. ... ... ... ... ... ... ... ...
13596 2022-01-04 22:50 17 18 22 26 27 ... 64 65 71 72 73 76 80
13597 2022-01-05 15:00 1 5 8 14 15 ... 47 54 59 67 70 72 76
13598 2022-01-05 22:50 6 7 14 15 16 ... 54 55 59 61 70 71 80
13599 2022-01-06 15:00 9 10 11 17 28 ... 51 55 65 67 72 76 78
13600 2022-01-06 22:50 1 2 6 9 11 ... 51 52 54 67 68 73 75
但我正在尝试通过在最后一层使用 softmax 函数和 SparseCategoricalCrossentropy()[=33= 来开发该 1D CNN 模型的修改版本] 作为损失函数，并通过向该代码添加新函数使其与众不同。
这是我目前的代码以及我正在尝试构建和使用的模型：
# multivariate output 1d cnn example
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # or any {'0', '1', '2'}
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
# multivariate output 1d cnn example
from numpy import array
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import *
from tensorflow.keras.losses import *
from tensorflow.keras.layers import *
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.callbacks import ReduceLROnPlateau
from tensorflow.keras.callbacks import ModelCheckpoint
# Define the Required Callback Function
class printlearningrate(tf.keras.callbacks.Callback):
def on_epoch_end (self, epoch, logs={}):
optimizer = self.model.optimizer
lr = K.eval(optimizer.lr)
Epoch_count = epoch + 1
print('\n', "Epoch:", Epoch_count, ', Learning Rate: {:.7f}'.format(lr))
printlr = printlearningrate()
# split a multivariate sequence into samples
def split_sequences (sequences, n_steps):
X, y = list(), list()
for i in range(len(sequences)):
# find the end of this pattern
end_ix = i + n_steps
# check if we are beyond the dataset
if end_ix > len(sequences) - 1:
break
# gather input and output parts of the pattern
seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
df = pd.read_csv('DrawsDB.csv')
print(df)
# df['Time'] = df[['Day', 'Hour']].agg(' '.join, axis=1)
df.insert(0, 'Time', df[['Day', 'Hour']].agg(' '.join, axis=1))
df.drop(columns=['Day', 'Hour'], inplace=True)
df.set_index('Time', inplace=True)
print(df)
numpy_array = df.to_numpy()
print(type(numpy_array))
print(numpy_array)
# choose a number of time steps
n_steps = 10
# convert into input/output
X, y = split_sequences(numpy_array, n_steps)
print(X.shape, y.shape)
# the dataset knows the number of features, e.g. 2
n_features = X.shape[2]
# Reduce learning rate when nothing happens to lower more the loss:
reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.9888888888888889,
patience=10, min_lr=0.0000001, verbose=1)
epochs = 10
# saving best model every epoch with ModelCheckpoint:
checkpoint_filepath = 'C:\Path\To\Saved\CheckPoint\model\'
model_checkpoint_callback = ModelCheckpoint(
filepath=checkpoint_filepath,
monitor='loss',
save_best_only=True,
save_weights_only=True,
verbose=1)
# define model
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=2, activation=LeakyReLU(), input_shape=(n_steps, n_features)))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(50, activation=LeakyReLU()))
model.add(Dense(n_features))
model.compile(optimizer=Nadam(lr=0.09), loss=SparseCategoricalCrossentropy(),
metrics=['accuracy', mean_squared_error, mean_absolute_error, mean_absolute_percentage_error])
# fit model
model.fit(X, y, epochs=10, verbose=2, callbacks=[printlr, reduce_lr, model_checkpoint_callback])
split_sequences 函数就像它的名字说的那样，它通过仅从数据库中获取 N 行作为输入并尝试从中预测所有 N+1 行来拆分数据库数据库作为输出。
但是，我认为存在问题，因为每次我尝试 运行 python 脚本时都会收到此错误：
tensorflow.python.framework.errors_impl.InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [32,20] and labels shape [640]
[[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits
(defined at C:\Users\UserName\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\backend.py:5114)
]] [Op:__inference_train_function_1228]
请问您知道如何解决这个问题吗？
提前致谢！
假设标签是整数，它们的形状不适合 SparseCategoricalCrossentropy。检查 docs。
尝试将您的 y 转换为 one-hot 编码标签：
y = tf.keras.utils.to_categorical(y, num_classes=20)
并将损失函数更改为 CategoricalCrossentropy:
model.compile(optimizer=Nadam(lr=0.09), loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=['accuracy', mean_squared_error, mean_absolute_error, mean_absolute_percentage_error])
它应该可以工作。