tensorflow model.load problems - python-3.x

Question

I was working on tensorflow and tried to save and load a model. model resides in below file
model = keras.Sequential()
model.add(keras.layers.Dense(785, activation ='sigmoid' ))
model.add(keras.layers.Dense(25, activation = 'sigmoid'))
model.add(keras.layers.Dense(10, activation = 'sigmoid'))
model.compile(optimizer=tf.train.GradientDescentOptimizer(0.01),
loss='mse',
metrics=['mae'])
model.fit(X,Y,epochs = 20, callbacks=[history])
f = h5py.File(r'C:\Users\akash\Desktop\Big Data\Model\model1', "w")
tf.keras.models.save_model(
model,
f,
overwrite=True,
include_optimizer=True
)
and my load file is as below
model1 = tf.keras.models.load_model(
r'C:\Users\akash\Desktop\Big Data\Model\model1',
custom_objects=None,
compile=True
)
model1.compile(optimizer=tf.train.GradientDescentOptimizer(0.01),
loss='mse',
metrics=['mae'])
I had to compile my model again as tensorflow requires you to do so and does not allow optimisers to be saved
and due to this im getting the below error
Using TensorFlow backend.
WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.
Traceback (most recent call last):
File "C:/Users/akash/Desktop/Big Data/scripts/load_model.py", line 21, in <module>
metrics=['mae'])
File "C:\Python\lib\site-packages\tensorflow\python\training\checkpointable\base.py", line 426, in _method_wrapper
method(self, *args, **kwargs)
File "C:\Python\lib\site-packages\tensorflow\python\keras\engine\training.py", line 525, in compile
metrics, self.output_names)
AttributeError: 'Sequential' object has no attribute 'output_names'

Answer 1

Maybe this helps you:
# MLP for Pima Indians Dataset Serialize to JSON and HDF5
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_json
import numpy
import os
# fix random seed for reproducibility
numpy.random.seed(7)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10, verbose=0)
# evaluate the model
scores = model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")
# later...
# load json and create model
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model.h5")
print("Loaded model from disk")
# evaluate loaded model on test data
loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
score = loaded_model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))