Created
January 2, 2018 07:24
-
-
Save avivl/3f3e876286670e19698a4bcc60f2313d to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# shuffle our features and turn into np.array as tensorflow takes in numpy array | |
random.shuffle(training) | |
training = np.array(training) | |
# trainX contains the Bag of words and train_y contains the label/ category | |
train_x = list(training[:, 0]) | |
train_y = list(training[:, 1]) | |
# reset underlying graph data | |
tf.reset_default_graph() | |
# Build neural network | |
net = tflearn.input_data(shape=[None, len(train_x[0])]) | |
net = tflearn.fully_connected(net, 8) | |
net = tflearn.fully_connected(net, 8) | |
net = tflearn.fully_connected(net, len(train_y[0]), activation='softmax') | |
net = tflearn.regression(net) | |
# Define model and setup tensorboard | |
model = tflearn.DNN(net, tensorboard_dir='tflearn_logs') | |
# Start training (apply gradient descent algorithm) | |
model.fit(train_x, train_y, n_epoch=1000, batch_size=8, show_metric=True) | |
model.save('model.tflearn') |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment