问自定义丢失函数Tensorflow 2 [Keras符号输入/输出没有实现]

EN

import numpy as np
import matplotlib.pyplot as plt

from keras.layers import Input, Dense
from keras.models import Model
import tensorflow as tf


def oscillator(d_, w0_, x):
    assert d_ < w0_
    w = np.sqrt(w0_**2 - d_**2)
    phi = np.arctan(-d_/w)
    A = 1/(2*np.cos(phi))
    cos = np.cos(phi+w*x)
    sin = np.sin(phi+w*x)
    exp = np.exp(-d_*x)
    return exp*2*A*cos



# PARAMETERS:
np.random.seed(5)
N = 20
epochs = 2000
d, w0 = 2, 20
nn_dim = 64


# DATA:
x = np.linspace(0,1,100)
y = oscillator(d,w0,x)

x_train = np.sort(np.random.uniform(0,0.35,N)[:,np.newaxis], axis=0)
y_train = oscillator(d,w0,x_train)

tf_y = tf.Variable(y_train,dtype=tf.float32)



# LAYERS:
input_layer = Input(shape=(1,))
Layer_1 = Dense(nn_dim, activation="tanh")(input_layer)
Layer_2 = Dense(nn_dim, activation="tanh")(Layer_1)
output_layer = Dense(1)(Layer_2)
model = Model(inputs=input_layer, outputs=output_layer)



loss_func = tf.reduce_mean(tf.math.squared_difference(tf_y,output_layer))
model.compile(optimizer='adam', loss=loss_func, metrics=['mse'])


md = model.fit(x_train,y_train,epochs=epochs,verbose=1)
y_pred = model.predict(x[:,np.newaxis])


# PLOTTING:
fig = plt.figure()
plt.plot(md.history['loss'], label='training')
plt.legend()

plt.figure()
plt.plot(x,y,label="Exact solution")
plt.scatter(x_train,y_train,label="Data",color="orange")
plt.plot(x,y_pred,label="Prediction",linestyle="--",color="red")
plt.legend()
plt.show()