问model = tf.keras.models.load_model()

EN

#define model
model = Sequential()
model.add(Dense(80, input_dim=2, kernel_initializer='normal', activation='relu'))
model.add(Dense(60, kernel_initializer='normal', activation='relu'))
model.add(Dense(40, kernel_initializer='normal', activation='relu'))
model.add(Dense(20, kernel_initializer='normal', activation='relu'))
model.add(Dense(10, kernel_initializer='normal', activation='relu'))
model.add(Dense(5, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
model.summary()
model.compile(loss='mse', optimizer='adam', metrics=[rmse])



# train model, test callback option
history = model.fit(X_train, Y_train, epochs=75, batch_size=1, verbose=2, callbacks=[callback])
#history = model.fit(X_train, Y_train, epochs=60, batch_size=1, verbose=2)

# plot metrics
plt.plot(history.history['rmse'])
plt.title('kW RSME Vs Epoch')
plt.show()


model.save('./saved_model/kwSummer')

model = tf.keras.models.load_model('./saved_model/kwSummer')

# Check its architecture
new_model.summary()

ValueError: Unable to restore custom object of type _tf_keras_metric currently. Please make sure that the layer implements `get_config`and `from_config` when saving. In addition, please use the `custom_objects` arg when calling `load_model()`.

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras import backend

from datetime import datetime
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import seaborn as sns
import math


df = pd.read_csv('./colabData.csv', index_col='Date', parse_dates=True)

print(df.info())



# This function keeps the learning rate at 0.001
# and decreases it exponentially after that.
def scheduler(epoch):
  if epoch < 1:
    return 0.001
  else:
    return 0.001 * tf.math.exp(0.01 * (1 - epoch))

callback = tf.keras.callbacks.LearningRateScheduler(scheduler)


#function to calculate RSME
def rmse(y_true, y_pred):
    return backend.sqrt(backend.mean(backend.square(y_pred - y_true), axis=-1))




dfTrain = df.copy()

# split into input (X) and output (Y) variables
X = dfTrain.drop(['kW'],1)
Y = dfTrain['kW']

#define training & testing data set
offset = int(X.shape[0] * 0.8)
X_train, Y_train = X[:offset], Y[:offset]
X_test, Y_test = X[offset:], Y[offset:]


#define model
model = Sequential()
model.add(Dense(80, input_dim=2, kernel_initializer='normal', activation='relu'))
model.add(Dense(60, kernel_initializer='normal', activation='relu'))
model.add(Dense(40, kernel_initializer='normal', activation='relu'))
model.add(Dense(20, kernel_initializer='normal', activation='relu'))
model.add(Dense(10, kernel_initializer='normal', activation='relu'))
model.add(Dense(5, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
model.summary()
model.compile(loss='mse', optimizer='adam', metrics=[rmse])



# train model, test callback option
history = model.fit(X_train, Y_train, epochs=75, batch_size=1, verbose=2, callbacks=[callback])
#history = model.fit(X_train, Y_train, epochs=60, batch_size=1, verbose=2)

# plot metrics
plt.plot(history.history['rmse'])
plt.title('kW RSME Vs Epoch')
plt.show()

model.save('./saved_model/kwSummer')
print('[INFO] Saved model to drive')