当使用Keras预测函数时，预测数据集的输入形状似乎正在更改（预测似乎向第一维添加了“无”）。

scaler = MinMaxScaler()
scaler2 = MinMaxScaler()

normalized_data = scaler.fit_transform(dataset)
normalized_predict_data = scaler2.fit_transform(predict_dataset)

x = normalized_data[:, 0:90]
y = normalized_data[:, 90]

z = normalized_predict_data[:, 0:90]
print(z.shape)

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=10)
print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)

model = Sequential()
model.add(Dense(4, input_dim=90, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(16, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

history = model.fit(x_train, y_train, validation_split=0.33, epochs=50, batch_size=100, verbose=0)

loss, accuracy = model.evaluate(x_test, y_test, verbose=0)
print("Model loss: %.2f, Accuracy: %.2f" % ((loss * 100), (accuracy * 100)))

Xnew = z
ynew = model.predict(array([Xnew]))

for item in Xnew:
    print("X=%s, Predicted=%s" % (item, ynew[0]))

当调用print函数显示预测数据集的形状时，将按预期打印输出（2，90）（2行数据和90个输入）
当尝试使用predict函数时，它会打印以下错误：

ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 90), found shape=(None, 2, 90)