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comet-keras-mnist-example.py
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comet-keras-mnist-example.py
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# Trains a simple deep NN on the MNIST dataset.
from __future__ import print_function
# pre install comet_ml by running : pip install comet_ml
# make sure comet_ml is the first import (before all other Machine learning lib)
from comet_ml import Experiment
import os
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
def main():
num_classes = 10
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
train(x_train, y_train, x_test, y_test)
def build_model_graph(input_shape=(784,)):
model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(), metrics=['accuracy'])
return model
def train(x_train, y_train, x_test, y_test):
# Define model
model = build_model_graph()
# Setting the API key (saved as environment variable)
experiment = Experiment(
#api_key="YOUR API KEY",
# or
api_key=os.environ.get("COMET_API_KEY"),
project_name='comet-examples')
experiment.log_dataset_hash(x_train)
# and thats it... when you run your code all relevant data will be tracked and logged in https://www.comet.ml/view/YOUR-API-KEY
model.fit(x_train, y_train, batch_size=128,
epochs=50, validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
if __name__ == '__main__':
main()