We want to export our model for Keras from Fabrik.
- First, select the 2nd button from the left in the Actions section of the sidebar.
- A drop-down list should appear. Select Keras.
- This should download a JSON file to your computer.
-
Rename the file to
model.json
. -
Load the model from the JSON file using the following code:
from keras.models import model_from_json # Read and load the JSON file json_file = open('<path_to_file>/model.json', 'r') loaded_model_json = json_file.read() json_file.close() # Use Keras's built in model_from_json function to convert the JSON file to a model loaded_model = model_from_json(loaded_model_json) # Print a summary of the model to verify that the model loaded correctly print (loaded_model.summary())
Example1
-
Export this example Keras model (name it
model.json
). -
Download this data set that we will use to train on (name it
pima-indians-diabetes.csv
). -
Create a python file (name it
kerasJSONLoader.py
) and insert the following code:from keras.models import model_from_json import numpy import os # Fix random seed to allow similar accuracy measures at the end numpy.random.seed(7) # Load pima indians dataset dataset = numpy.loadtxt('<path_to_file>/pima-indians-diabetes.csv', delimiter=',') # Split the dataset into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # Load the model from JSON file json_file = open('<path_to_file>/model.json', 'r') loaded_model_json = json_file.read() json_file.close() loaded_model = model_from_json(loaded_model_json) # Configure model for training and testing with accuracy evaluation loaded_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # Train the model loaded_model.fit(X, Y, epochs=150, batch_size=10, verbose=0) # Evaluate the model scores = loaded_model.evaluate(X, Y, verbose=0) # Print final accuracy print("%s: %.2f%%" % (loaded_model.metrics_names[1], scores[1] * 100))
-
Then run the code in terminal.
python <path_to_file>/kerasJSONLoader.py
You should be getting around 76-78% accuracy.
This code trains and evaluates the loaded model on the dataset.
keras_sample_cifar10.py
The code sample loads the model specified by the user and trains and evaluates the model.
It uses keras.datasets.cifar10
as the dataset.
To run the code, run
python keras_sample_cifar10.py MODEL_NAME
Replace MODEL_NAME
with the model that you want to use.