我正在嘗試為多類分類構建模型，但我不明白如何設定正確的輸入形狀。我有一個帶形狀的訓練集，(5420, 212)這是我構建的模型：
model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape = (5420,)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(5, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
history = model.fit(X_train, y_train, epochs=20, batch_size=512)
當我運行它時，我收到錯誤：
ValueError: Input 0 of layer sequential_9 is incompatible with the layer: expected axis -1 of input shape to have value 5420 but received input with shape (None, 212)
為什么？輸入值不正確嗎？
uj5u.com熱心網友回復：
輸入形狀應等于輸入X第二維的長度，而輸出形狀應等于輸出Y第二維的長度（假設X和Y都是二維的，即它們沒有更高的維度） .
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.datasets import make_classification
from sklearn.preprocessing import OneHotEncoder
tf.random.set_seed(0)
# generate some data
X, y = make_classification(n_classes=5, n_samples=5420, n_features=212, n_informative=212, n_redundant=0, random_state=42)
print(X.shape, y.shape)
# (5420, 212) (5420,)
# one-hot encode the target
Y = OneHotEncoder(sparse=False).fit_transform(y.reshape(-1, 1))
print(X.shape, Y.shape)
# (5420, 212) (5420, 5)
# extract the input and output shapes
input_shape = X.shape[1]
output_shape = Y.shape[1]
print(input_shape, output_shape)
# 212 5
# define the model
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(input_shape,)))
model.add(Dense(64, activation='relu'))
model.add(Dense(output_shape, activation='softmax'))
# compile the model
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
# fit the model
history = model.fit(X, Y, epochs=3, batch_size=512)
# Epoch 1/3
# 11/11 [==============================] - 0s 1ms/step - loss: 4.8206 - accuracy: 0.2208
# Epoch 2/3
# 11/11 [==============================] - 0s 1ms/step - loss: 2.8060 - accuracy: 0.3229
# Epoch 3/3
# 11/11 [==============================] - 0s 1ms/step - loss: 2.0705 - accuracy: 0.3989
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