python - 如何在 Keras API 中将数组列表作为输入

Question

嗯，我是机器学习的新手，Keras 也是如此。我正在尝试创建一个模型，可以将数组的数组列表(2 个数组中的 6400 个数组的列表)作为输入传递。 这是我的代码的问题:

XFIT = np.array([x_train, XX_train])
YFIT = np.array([y_train, yy_train])
Inputs = keras.layers.Input(shape=(6400, 2))
hidden1 = keras.layers.Dense(units=100, activation="sigmoid")(Inputs)
hidden2 = keras.layers.Dense(units=100, activation='relu')(hidden1)
predictions = keras.layers.Dense(units=3, activation='softmax')(hidden2)

model = keras.Model(inputs=Inputs, outputs=predictions)

没有错误；但是，输入层 (Inputs) 强制我传递 (6400, 2) 形状，因为每个数组(x_train 和 XX_train)内部有 6400 个数组。完成 epoch 后的结果是这样的:

Train on 2 samples
Epoch 1/5

2/2 [==============================] - 1s 353ms/sample - loss: 1.1966 - accuracy: 0.2488
Epoch 2/5

2/2 [==============================] - 0s 9ms/sample - loss: 1.1303 - accuracy: 0.2544
Epoch 3/5

2/2 [==============================] - 0s 9ms/sample - loss: 1.0982 - accuracy: 0.3745
Epoch 4/5

2/2 [==============================] - 0s 9ms/sample - loss: 1.0854 - accuracy: 0.3745
Epoch 5/5

2/2 [==============================] - 0s 9ms/sample - loss: 1.0835 - accuracy: 0.3745

Process finished with exit code 0

由于输入形状的原因，我不能在每个时期训练超过两次。我怎样才能改变这个输入？ 我尝试过其他形状，但它们给了我错误。

x_train，XX_train看起来像这样

[[[0.505834 0.795461]
  [0.843175 0.975741]
  [0.22349  0.035036]
  ...
  [0.884796 0.867509]
  [0.396942 0.659936]
  [0.873194 0.05454 ]]

 [[0.95968  0.281957]
  [0.137547 0.390005]
  [0.635382 0.901555]
  ...
  [0.887062 0.486206]
  [0.49827  0.949123]
  [0.034411 0.983711]]]

谢谢你，如果我犯了任何错误，请原谅我，第一次使用 Keras，第一次使用 StackOverFlow :D

Answer 1

你就快到了。问题在于:

XFIT = np.array([x_train, XX_train])
YFIT = np.array([y_train, yy_train])

让我们看一个例子:

import numpy as np

x_train = np.random.random((6400, 2))
y_train = np.random.randint(2, size=(6400,1))

xx_train = np.array([x_train, x_train])
yy_train = np.array([y_train, y_train])

print(xx_train.shape)
(2, 6400, 2)

print(yy_train.shape)
(2, 6400, 1)

在数组中，我们有 2 个批处理，每个批处理有 6400 个样本。这意味着当我们调用 model.fit 时，它只有 2 个批处理需要训练。相反，我们可以做什么:

xx_train = np.vstack([x_train, x_train])
yy_train = np.vstack([y_train, y_train])

print(xx_train.shape)
(12800, 2)

print(yy_train.shape)
(12800, 1)

现在，我们已经正确连接了两个样本，现在可以进行训练了。

Inputs = Input(shape=(2, ))
hidden1 = Dense(units=100, activation="sigmoid")(Inputs)
hidden2 = Dense(units=100, activation='relu')(hidden1)
predictions = Dense(units=1, activation='sigmoid')(hidden2)

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

model.fit(xx_train, yy_train, batch_size=10, epochs=5)

Train on 12800 samples
Epoch 1/5
12800/12800 [==============================] - 3s 216us/sample - loss: 0.6978 - acc: 0.5047
Epoch 2/5
12800/12800 [==============================] - 2s 186us/sample - loss: 0.6952 - acc: 0.5018
Epoch 3/5
12800/12800 [==============================] - 3s 196us/sample - loss: 0.6942 - acc: 0.4962
Epoch 4/5
12800/12800 [==============================] - 3s 217us/sample - loss: 0.6938 - acc: 0.4898
Epoch 5/5
12800/12800 [==============================] - 3s 217us/sample - loss: 0.6933 - acc: 0.5002