具有自定义输入的 Keras 神经网络

Question

我想要完成的是在一些自定义输入数据和输出数据之间设置关系，然后让神经网络计算出这个 relationship/rule 以预测给定输入的未来输出。我在这里设置了一些测试代码，其中生成随机输入列表，如果大于 0.5，则输出为 1，否则输出为 0。

from tensorflow import keras
import numpy as np

# generate data
data_input_generate = np.random.random((6400, 1))
data_output_generate = np.random.randint(2, size=(6400, 1))
data_input = np.vstack([data_input_generate, data_input_generate])
data_output = np.vstack([data_output_generate, data_output_generate])

for i in range(len(data_input)):

    if data_input[i] >= 0.5:
        data_output[i] = [1]
    else:
        data_output[i] = [0]


# setup neural network
Inputs = keras.layers.Input(shape=(1, ))
hidden1 = keras.layers.Dense(units=100, activation="sigmoid")(Inputs)
hidden2 = keras.layers.Dense(units=100, activation='softmax')(hidden1)
predictions = keras.layers.Dense(units=1, activation='relu')(hidden2)

# initialize model
model = keras.Model([Inputs], outputs=predictions)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# fit
model.fit(data_input, data_output, batch_size=10, epochs=5)

# predict
predictions = model.predict(data_input_generate)

# print predictions
for i in range(10):
    print(f"Value: {data_input_generate[i]}, Result: {data_output_generate[i]}, Prediction: {predictions[i]}")

问题是，拟合模型后，准确率停留在 50%。这是我的层激活函数有问题还是我设置模型的方式有问题？我的目标是以相当高的准确度正确预测输出。提前致谢！

Answer 1

尝试在输出层上使用 sigmoid 激活函数。这是一个工作示例：

from tensorflow import keras
import numpy as np

# generate data
data_input_generate = np.random.random((6400, 1))
data_output_generate = np.random.randint(2, size=(6400, 1))
data_input = np.vstack([data_input_generate, data_input_generate])
data_output = np.vstack([data_output_generate, data_output_generate])

for i in range(len(data_input)):

    if data_input[i] >= 0.5:
        data_output[i] = [1]
    else:
        data_output[i] = [0]


# setup neural network
Inputs = keras.layers.Input(shape=(1, ))
hidden1 = keras.layers.Dense(units=64, activation="relu")(Inputs)
hidden2 = keras.layers.Dense(units=32, activation='relu')(hidden1)
dropout = keras.layers.Dropout(0.8)(hidden2)
predictions = keras.layers.Dense(units=1, activation='sigmoid')(dropout)

# initialize model
model = keras.Model([Inputs], outputs=predictions)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# fit
model.fit(data_input, data_output, batch_size=10, epochs=5)

# predict
predictions = model.predict(data_input_generate)

# print predictions
for i in range(10):
    print(f"Value: {data_input_generate[i]}, Result: {data_output_generate[i]}, Prediction: {predictions[i]}")

Epoch 1/5
1280/1280 [==============================] - 4s 3ms/step - loss: 0.3632 - accuracy: 0.8327
Epoch 2/5
1280/1280 [==============================] - 3s 3ms/step - loss: 0.1870 - accuracy: 0.9427
Epoch 3/5
1280/1280 [==============================] - 3s 3ms/step - loss: 0.1528 - accuracy: 0.9475
Epoch 4/5
1280/1280 [==============================] - 3s 2ms/step - loss: 0.1461 - accuracy: 0.9482
Epoch 5/5
1280/1280 [==============================] - 2s 2ms/step - loss: 0.1384 - accuracy: 0.9493
Value: [0.79415764], Result: [0], Prediction: [0.9997529]
Value: [0.38311113], Result: [1], Prediction: [1.7478478e-05]
Value: [0.05360975], Result: [0], Prediction: [2.3240638e-07]
Value: [0.78635261], Result: [1], Prediction: [0.99970365]
Value: [0.74414175], Result: [1], Prediction: [0.99921006]
Value: [0.47845171], Result: [1], Prediction: [0.07256863]
Value: [0.53008247], Result: [0], Prediction: [0.886382]
Value: [0.40377478], Result: [1], Prediction: [9.9769844e-05]
Value: [0.18209166], Result: [1], Prediction: [5.199377e-07]
Value: [0.00937745], Result: [1], Prediction: [1.7613968e-07]

具有自定义输入的 Keras 神经网络

Keras Neural Network With Custom Input

python

tensorflow

keras

neural-network