Keras model.predict 总是预测 1

Question

我正在做一些人工智能项目，我想预测比特币的趋势，但是在使用 Keras 的 model.predict 函数和我的 test_set 时，预测总是等于 1，因此我的图表中的线总是直。

import csv
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from cryptory import Cryptory
from keras.models import Sequential, Model, InputLayer
from keras.layers import LSTM, Dropout, Dense
from sklearn.preprocessing import MinMaxScaler


def format_to_3d(df_to_reshape):
    reshaped_df = np.array(df_to_reshape)
    return np.reshape(reshaped_df, (reshaped_df.shape[0], 1, reshaped_df.shape[1]))


crypto_data = Cryptory(from_date = "2014-01-01")
bitcoin_data = crypto_data.extract_coinmarketcap("bitcoin")

sc = MinMaxScaler()

for col in bitcoin_data.columns:
    if col != "open":
        del bitcoin_data[col]

training_set = bitcoin_data;
training_set = sc.fit_transform(training_set)

# Split the data into train, validate and test
train_data = training_set[365:]

# Split the data into x and y
x_train, y_train = train_data[:len(train_data)-1], train_data[1:]

model = Sequential()
model.add(LSTM(units=4, input_shape=(None, 1))) # 128 -- neurons**?
# model.add(Dropout(0.2))
model.add(Dense(units=1, activation="softmax"))  # activation function could be different
model.compile(optimizer="adam", loss="mean_squared_error")  # mse could be used for loss, look into optimiser

model.fit(format_to_3d(x_train), y_train, batch_size=32, epochs=15)

test_set = bitcoin_data
test_set = sc.transform(test_set)
test_data = test_set[:364]

input = test_data
input = sc.inverse_transform(input)
input = np.reshape(input, (364, 1, 1))

predicted_result = model.predict(input)
print(predicted_result)

real_value = sc.inverse_transform(input)

plt.plot(real_value, color='pink', label='Real Price')
plt.plot(predicted_result, color='blue', label='Predicted Price')
plt.title('Bitcoin Prediction')
plt.xlabel('Time')
plt.ylabel('Prices')
plt.legend()
plt.show()

训练集表现如下：

1566/1566 [==============================] - 3s 2ms/step - loss: 0.8572
Epoch 2/15
1566/1566 [==============================] - 1s 406us/step - loss: 0.8572
Epoch 3/15
1566/1566 [==============================] - 1s 388us/step - loss: 0.8572
Epoch 4/15
1566/1566 [==============================] - 1s 388us/step - loss: 0.8572
Epoch 5/15
1566/1566 [==============================] - 1s 389us/step - loss: 0.8572
Epoch 6/15
1566/1566 [==============================] - 1s 392us/step - loss: 0.8572
Epoch 7/15
1566/1566 [==============================] - 1s 408us/step - loss: 0.8572
Epoch 8/15
1566/1566 [==============================] - 1s 459us/step - loss: 0.8572
Epoch 9/15
1566/1566 [==============================] - 1s 400us/step - loss: 0.8572
Epoch 10/15
1566/1566 [==============================] - 1s 410us/step - loss: 0.8572
Epoch 11/15
1566/1566 [==============================] - 1s 395us/step - loss: 0.8572
Epoch 12/15
1566/1566 [==============================] - 1s 386us/step - loss: 0.8572
Epoch 13/15
1566/1566 [==============================] - 1s 385us/step - loss: 0.8572
Epoch 14/15
1566/1566 [==============================] - 1s 393us/step - loss: 0.8572
Epoch 15/15
1566/1566 [==============================] - 1s 397us/step - loss: 0.8572

我应该用实际价格和预测价格打印一个图，实际价格显示正确，但预测价格只是一条直线，因为 model.predict 只包含值 1。

提前致谢！

Answer 1

您正在尝试预测价格值，也就是说，您的目标是解决回归问题而不是分类问题。

但是，在网络的最后一层 (model.add(Dense(units=1, activation="softmax"))) 中，您只有一个神经元（这足以解决回归问题），但您选择使用 softmax 激活函数。 softmax 函数用于多类分类问题，将输出归一化为概率分布。如果你有一个单一的输出神经元并且你应用了 softmax，那么最终的结果总是 1.0，因为它是概率分布的唯一参数。

总之，对于回归问题，您不使用激活函数，因为网络旨在已经输出预测值。