我寫了一個帶有神經網路近似多項式的小腳本，并在每個時期繪制結果，但問題是我希望每次迭代新圖都會覆寫以前的圖，所以我可以看到它在訓練中的變化。
我在網上搜索，發現我需要使用 ion() 或 isinteractive() 或 clear()，但我都嘗試了它們，它仍然不起作用。
編輯： 為了澄清起見，我使用的是 Jupyter notebook，所以我希望它在這個平臺上作業。
這是我的代碼：
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense
from numpy import asarray
from matplotlib import pyplot
from tensorflow.keras.layers import Conv1D
import tensorflow
class myCallback(tensorflow.keras.callbacks.Callback):
def on_train_begin(self, logs={}):
pyplot.ion()
def on_epoch_end(self, epoch, logs=None):
yhat = model.predict(x)
# inverse transforms
x_plot = scale_x.inverse_transform(x)
y_plot = scale_y.inverse_transform(y)
yhat_plot = scale_y.inverse_transform(yhat)
# report model error
print('MSE: %.3f' % mean_squared_error(y_plot, yhat_plot))
# plot x vs y
plt = pyplot.scatter(x_plot,y_plot, label='Actual')
# plot x vs yhat
pyplot.scatter(x_plot,yhat_plot, label='Predicted')
pyplot.title('Input (x) versus Output (y)')
pyplot.xlabel('Input Variable (x)')
pyplot.ylabel('Output Variable (y)')
pyplot.legend()
pyplot.show()
# define the dataset
x = asarray([i for i in range(-50,51)])
y = asarray([i**3 for i in x])
print(x.min(), x.max(), y.min(), y.max())
# reshape arrays into into rows and cols
x = x.reshape((len(x), 1))
y = y.reshape((len(y), 1))
# separately scale the input and output variables
scale_x = MinMaxScaler()
x = scale_x.fit_transform(x)
scale_y = MinMaxScaler()
y = scale_y.fit_transform(y)
print(x.min(), x.max(), y.min(), y.max())
# design the neural network model
model = Sequential()
model.add(Dense(10, input_dim=1, activation='relu', kernel_initializer='he_uniform'))
#Conv1D(32, 5, activation='relu')
model.add(Dense(10, activation='relu', kernel_initializer='he_uniform'))
model.add(Dense(1))
opt = tensorflow.keras.optimizers.Adam(learning_rate=0.01)
# define the loss function and optimization algorithm
model.compile(loss='mse', optimizer=opt)
# ft the model on the training dataset
model.fit(x, y, epochs=10, batch_size=10, verbose=0, callbacks=[myCallback()])
# make predictions for the input data
您的幫助將不勝感激！
uj5u.com熱心網友回復：
在每個 epoch 之后你都會得到一個新的情節，但這些變化并不明顯，因為你的模型太弱了。這是一個具有顯著差異的示例：
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense
from numpy import asarray
from matplotlib import pyplot
import tensorflow
from IPython.display import clear_output
class myCallback(tensorflow.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs=None):
clear_output(wait=True)
yhat = model.predict(x)
# inverse transforms
x_plot = scale_x.inverse_transform(x)
y_plot = scale_y.inverse_transform(y)
yhat_plot = scale_y.inverse_transform(yhat)
# report model error
print('MSE: %.3f' % mean_squared_error(y_plot, yhat_plot))
# plot x vs y
plt = pyplot.scatter(x_plot,y_plot, label='Actual')
# plot x vs yhat
pyplot.scatter(x_plot,yhat_plot, label='Predicted')
pyplot.title('Input (x) versus Output (y)')
pyplot.xlabel('Input Variable (x)')
pyplot.ylabel('Output Variable (y)')
pyplot.legend()
pyplot.show()
# define the dataset
x = asarray([i for i in range(-50,51)])
y = asarray([i**3 for i in x])
print(x.shape)
print(x.min(), x.max(), y.min(), y.max())
# reshape arrays into into rows and cols
x = x.reshape((len(x), 1))
y = y.reshape((len(y), 1))
# separately scale the input and output variables
scale_x = MinMaxScaler()
x = scale_x.fit_transform(x)
scale_y = MinMaxScaler()
y = scale_y.fit_transform(y)
print(x.min(), x.max(), y.min(), y.max())
# design the neural network model
model = Sequential()
model.add(Dense(64, input_dim=1, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(16, activation='relu'))
model.add(Dense(1))
opt = tensorflow.keras.optimizers.Adam(learning_rate=0.01)
# define the loss function and optimization algorithm
model.compile(loss='mse', optimizer=opt)
# ft the model on the training dataset
model.fit(x, y, epochs=50, batch_size=10, verbose=0, callbacks=[myCallback()])
# make predictions for the input data
這是最后一個時代的情節：
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標籤：Python python-3.x 张量流 matplotlib 喀拉斯