recurrent-neural-network - 输入形状的预期轴 -1 的值为 20，但接收到的输入形状为（无，29）

Question

ValueError: 层序 66 的输入 0 与层不兼容：输入形状的预期轴 -1 具有值 20 但接收到形状为 (None, 29)
的输入 tensorflow as tf from tensorflow import keras from tensorflow.keras 从 keras 导入层.models import Sequential from keras.layers import Dense, Dropout, Activation from keras.optimizers import SGD

# Generate dummy data
import numpy as np
x_train = np.random.random((1000, 29))
y_train = keras.utils.to_categorical(np.random.randint(10, size=(1000, 1)), num_classes=10)
x_test = np.random.random((100, 20))
y_test = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)

model = Sequential()
# Dense(64) is a fully-connected layer with 64 hidden units.
# in the first layer, you must specify the expected input data shape:
# here, 20-dimensional vectors.
model.add(Dense(64, activation='relu', input_dim=20))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])

model.fit(x_train, y_train,
          epochs=20,
          batch_size=128)
score = model.evaluate(x_test, y_test, batch_size=128)

请为我解释！谢谢。

Answer 1

学习：

• Keras 模型基于输入数据和标签的 Numpy 数组构建。为了训练模型，我们通常使用函数。
• 用于多类 softmax 分类的多层人工神经网络 (MLP)： https ://keras.io/ko/getting-started/sequential-model-guide/

#x_train, và x_test có dạng 2 chiều nên số cột của x_train là số chiều vào cho mạng ở trên。