Question

我正在尝试使用用于多类分类任务的keras实现ANN。这是我的数据集：

特征形状（9498，17）

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。我想从一个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,
)

这将导致以下错误：

---------------------------------------------------------------------------
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此错误。另外，即使我检查了解决同一主题的其他类似文章，我似乎也不了解喀拉拉邦的输入和输出形状。

编辑：这是我的标签的快速浏览：

我应该喜欢一种热编码吗？

Answer 1

因此，这里的问题是标签数据是形状的一维矢量（9498，）。这意味着输出标签的数量为1，这将是一个回归问题，因为您仅预测一个值。

另一方面，我们可以看到该向量包含24种不同类别的标签，这是您要为多分类器建模的地方。因此，您首先要做的是将标签向量中的每个值转换为24维向量。例如，您可以使用scikit-learn LabelBinarizer来做到这一点，这是它的基本工作原理：

from sklearn.preprocessing import LabelBinarizer

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

现在您可以将其输入模型了

这也许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,
)

keras ANN中的输入和输出形状

1 个答案: