Htydjtk

I am been using a script from machinelearningmastery on Keras regression and I would like to save model as a .h5 file.

Machinelearningmastery also has another tutorial for saving models/pickles but the scripts are written in a model.fit() method in Keras… But the script I am using I am defining the model thru calling a function.

Can someone give me a tip on how I can save this model as a .h5df?

import numpy as np

import pandas as pd

from keras.models import Sequential

from keras.layers import 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 matplotlib.pyplot as plt

import math

from sklearn.preprocessing import MinMaxScaler





# load dataset

dataset = pd.read_csv("joinedRuntime2.csv", index_col='Date', parse_dates=True)



print(dataset.shape)

print(dataset.dtypes)

print(dataset.columns)



# shuffle dataset

df = dataset.sample(frac=1.0)



# split into input (X) and output (Y) variables

X = np.array(df.drop(['kWh'],1))

Y = np.array(df['kWh'])





def wider_model():

    # create model

    model = Sequential()

    model.add(Dense(20, input_dim=7, kernel_initializer='normal', activation='relu'))

    #model.add(Dense(28, kernel_initializer='normal', activation='relu'))

    #model.add(Dense(21, kernel_initializer='normal', activation='relu'))

    #model.add(Dense(14, kernel_initializer='normal', activation='relu'))

    model.add(Dense(10, kernel_initializer='normal', activation='relu'))

    model.add(Dense(1, kernel_initializer='normal'))

    # Compile model

    model.compile(loss='mean_squared_error', optimizer='adam')

    return model





# fix random seed for reproducibility

seed = 7

np.random.seed(seed)

estimators = 

estimators.append(('standardize', StandardScaler()))

estimators.append(('mlp', KerasRegressor(build_fn=wider_model, epochs=200, batch_size=5, verbose=0)))

pipeline = Pipeline(estimators)

kfold = KFold(n_splits=10, random_state=seed)

results = cross_val_score(pipeline, X, Y, cv=kfold)

print("Wider: %.2f (%.2f) MSE" % (results.mean(), results.std()))

print("RMSE", math.sqrt(results.std()))

After your edit, it sounds like the real issue is that you're using the sklearn wrapper for keras and an sklearn pipeline.

To access the actual NN from the pipeline, use the steps or named_steps attribute:
https://scikit-learn.org/stable/modules/compose.html#pipeline

Then, to save the wrapped KerasRegressor model, use model_name.model.save():
https://stackoverflow.com/questions/40396042/how-to-save-scikit-learn-keras-model-into-a-persistence-file-pickle-hd5-json-ya#40397312
https://github.com/keras-team/keras/issues/4274

Citing Keras' official page:

It is not recommended to use pickle or cPickle to save a Keras model.

You can use model.save(filepath) to save a Keras model into a single
HDF5 file which will contain:

• the architecture of the model, allowing to re-create the model


• the weights of the model


• the training configuration (loss, optimizer)


• the state of the optimizer, allowing to resume training exactly where you left off.