Skip to content

Latest commit

 

History

History
84 lines (64 loc) · 3.13 KB

keras_json_usage_1.md

File metadata and controls

84 lines (64 loc) · 3.13 KB
Raw

Basics of Using the Exported Keras Model

We want to export our model for Keras from Fabrik.

  1. First, select the 2nd button from the left in the Actions section of the sidebar.

  1. A drop-down list should appear. Select Keras.
    • This should download a JSON file to your computer.

  1. Rename the file to model.json.

  2. 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

  1. Export this example Keras model (name it model.json).

  2. Download this data set that we will use to train on (name it pima-indians-diabetes.csv).

  3. 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))

  4. 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.

Code template

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.