检查目标时出错:要求dense_1具有形状(5749，)，但得到具有形状(1，)的数组

import numpy as np

# 假设输入数据为 x
print(x.shape)  # 应该输出 (5749,)

# 假设使用Keras进行预测
predictions = model.predict(x_batch)
print(predictions.shape)  # 应该输出 (batch_size, 5749)

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

model = Sequential([
    Dense(64, activation='relu', input_shape=(input_dim,)),
    Dense(5749)  # 确保输出形状为 (None, 5749)
])

# 在关键步骤打印数据形状
print("Input shape:", x.shape)
predictions = model.predict(x)
print("Predictions shape:", predictions.shape)

import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# 假设输入数据的形状为 (num_samples, input_dim)
num_samples = 5749
input_dim = 10
x = np.random.rand(num_samples, input_dim)

# 定义模型
model = Sequential([
    Dense(64, activation='relu', input_shape=(input_dim,)),
    Dense(num_samples)  # 输出形状为 (None, 5749)
])

# 编译模型
model.compile(optimizer='adam', loss='mse')

# 检查输入数据形状
print("Input shape:", x.shape)

# 进行预测
predictions = model.predict(x)
print("Predictions shape:", predictions.shape)