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python - keras ANN 中的输入和输出形状

问题描述

我正在尝试使用 keras 为多类分类任务实现 ANN。这是我的数据集：

  1. 特征形状 (9498, 17)
  2. 标签形状 (9498,)

其中 9498 是像素数，17 是时间戳数，我有 24 个要预测的类。我想从一些非常基本的东西开始。这是我使用的代码：

import keras
from keras.models import Sequential
from keras.layers import Dense
# Loading the data
X_train, X_test, y_train, y_test = train_test_split(NDVI, labels, test_size=0.15, random_state=42)

# Building the model
model = Sequential([
  Dense(128, activation='relu', input_shape=(17,),name="layer1"),
  Dense(64, activation='relu', name="layer2"),
  Dense(24, activation='softmax', name="layer3"),
])
print(model.summary())


# Compiling the model
model.compile(
  optimizer='adam',                              # gradient-based optimizer
  loss='categorical_crossentropy',               # (>2 classes)
  metrics=['accuracy'],
)

# Training the model
model.fit(
  X_train, # training data
  y_train, # training targets
  epochs=5,
  batch_size=32,
)

这会导致以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-17-2f4cf6510b24> in <module>()
     23   y_train, # training targets
     24   epochs=5,
---> 25   batch_size=32,
     26 )

2 frames
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
   1152             sample_weight=sample_weight,
   1153             class_weight=class_weight,
-> 1154             batch_size=batch_size)
   1155 
   1156         # Prepare validation data.

/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    619                 feed_output_shapes,
    620                 check_batch_axis=False,  # Don't enforce the batch size.
--> 621                 exception_prefix='target')
    622 
    623             # Generate sample-wise weight values given the `sample_weight` and

/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    143                             ': expected ' + names[i] + ' to have shape ' +
    144                             str(shape) + ' but got array with shape ' +
--> 145                             str(data_shape))
    146     return data
    147 

ValueError: Error when checking target: expected layer3 to have shape (24,) but got array with shape (1,)

我不知道为什么会弹出这个错误。此外，即使我查看了其他解决同一主题的类似帖子，我似乎也不了解 keras 中的输入和输出形状。

编辑：这是我的标签的快速浏览：

快速查看我的标签

我应该喜欢一种热编码吗？

标签： pythonmachine-learningkeras

解决方案

所以这里的问题是您的标签数据是形状的一维向量（9498，）。这意味着输出标签的数量为 1，这将是一个回归问题，因为您只预测一个值。

另一方面，我们可以看到该向量包含 24 个不同类别的标签，这是您想要对多分类器进行建模的地方。所以你首先要做的就是将labels向量中的每一个值转换成一个24维向量。例如，您可以使用 the 来做到这一点，下面scikit-learn LabelBinarizer是它的基本工作方式：

from sklearn.preprocessing import LabelBinarizer

binarizer = LabelBinarizer()
labels = binarizer.fit_transform(labels)
print("binarized_labels.shape:", labels.shape) #-> should now return (9498, 24)

现在你可以把它喂给你的模型

这可能是一个完整的解决方案：

from sklearn.preprocessing import LabelBinarizer

import keras
from keras.models import Sequential
from keras.layers import Dense

# Loading the data
X_train, X_test, y_train, y_test = train_test_split(NDVI, labels, test_size=0.15, random_state=42)

# Binarize the output to have 24 class labels
binarizer = LabelBinarizer()
y_train = binarizer.fit_transform(y_train)
y_test = binarizer.transform(y_test) #N.B. here you neeed only to use '.transform()' rather that '.fit_transform()'


# Building the model
model = Sequential([
  Dense(128, activation='relu', input_shape=(17,),name="layer1"),
  Dense(64, activation='relu', name="layer2"),
  Dense(24, activation='softmax', name="layer3"),
])
print(model.summary())


# Compiling the model
model.compile(
  optimizer='adam',                              # gradient-based optimizer
  loss='categorical_crossentropy',               # (>2 classes)
  metrics=['accuracy'],
)

# Training the model
model.fit(
  X_train, # training data
  y_train, # training targets
  epochs=5,
  batch_size=32,
)

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