Question

我正在使用Keras进行预测。我使用两个数字阵列训练它，Y1和Y2用于每个输入数字X.

我想预测_Y1和_Y2，但我不知道如何。

可视化显示什么都没有。那讲得通。检查它，它看起来像＆＃34;预测＆＃34;数组是空的。

import numpy as np
import pandas
import math
import random
from keras.models import Sequential, Model
from keras.layers import Input, Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import graphviz
import pydot
import matplotlib.pyplot as plt 

X = np.array([])
Y_int = np.array([])
Y1 = np.array([])
Y2 = np.array([])


count = -1

while count < 1:
    count += 0.001
    X += np.array([count])
    i = ( math.sin(count) )
    Y_int += np.array([i])
    if i > 1 or i < -1:
        o = 1
        u = 1 / i
    else:
        o = i
        u = 1
    Y1 += np.array([o])
    Y2 += np.array([u])

length = len(X)

# define base model
def baseline_model():
    # create model
    model = Sequential()
    inp = Input((1,))
    x = Dense(100, kernel_initializer='lecun_normal', activation='tanh')(inp)
    out1 = Dense(1, kernel_initializer='lecun_normal')(x)
    out2 = Dense(1, kernel_initializer='lecun_normal')(x)
    model = Model(inp, [out1,out2])
    # Compile model
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model

estimator = baseline_model();
estimator.fit(X, [Y1, Y2], batch_size = 20, epochs = 1, verbose = 0)

plt.axis([-1, 1, -1, 1])
plt.ion()

while True:
    estimator.fit(X, [Y1, Y2], batch_size = 20, epochs = 1, verbose = 0)
    prediction = estimator.predict(X)
    plt.clf()
    plt.plot(X, Y_int)
    plt.plot(X, prediction )
    plt.pause(0.001)

Answer 1

+=不是附加numpy数组的正确方法（我认为这是你的目的）;这是您的第一个while循环的结果：

count = -1

while count < 1:
    count += 0.001
    X += np.array([count])
    i = ( math.sin(count) )
    Y_int += np.array([i])
    if i > 1 or i < -1:
        o = 1
        u = 1 / i
    else:
        o = i
        u = 1
    Y1 += np.array([o])
    Y2 += np.array([u])

length = len(X)

length
# 0
X
# array([], dtype=float64)
Y1
# array([], dtype=float64)
Y2
# array([], dtype=float64)

即。你的所有阵列都是空的......

如果追加数组确实是你的目的，请使用numpy.append方法：

count = -1

while count < 1:
    count += 0.001
    X = np.append(X, np.array([count]))
    i = math.sin(count))
    Y_int = np.append(Y_int, np.array([i]))
    if i > 1 or i < -1:
        o = 1
        u = 1 / i
    else:
        o = i
        u = 1
    Y1 = np.append(Y1, np.array([o]))
    Y2 = np.append(Y2, np.array([u]))

length = len(X)

length
# 2000
len(Y1), len(Y2), len(Y_int)
# (2000, 2000, 2000)

此外，您应该从模型定义中删除model = Sequential()行，因为您显然正在使用Keras Functional API。

Keras：输出数组搞砸了

1 个答案: