Skip to content

Latest commit

 

History

History
66 lines (56 loc) · 2.62 KB

README.md

File metadata and controls

66 lines (56 loc) · 2.62 KB
Raw

EVE

This repository is official implementation of We Don't Need No Adam, All We Need Is EVE: On The Variance of Dual Learning Rate And Beyond using TensorFlow.

EVE is equipped with two learning rates, where the 1st momentum is being obtained using short-term and long-term momenta, and 2nd momentum is the result of a residual learning paradigm. We have provided all the figures in interactive mode and you will be able to explore them on your DESKTOP browser by visiting EVE Optimiser Interactive Figures. The users and the community are encouraged to download these files and visualise the performance of the Adam optimiser vs. EVE in more granular detail.

How to use?!

Assuming you have built a TensorFlow model named model, you will be able to compile the model using EVE as follows:

from eve import EVE
...
model.compile(optimizer = EVE(learning_rate_1 = 0.001, learning_rate_2 = 0.0004),
              loss = ..., metrics = ...)
history = model.fit(...)
...

side note for TPU usage

We have made the flower classification dataset available on GCS bucket. Follow the steps below to load the data into your Colab Notebook. Initialize TPU:

import tensorflow as tf
# Detect hardware, return appropriate distribution strategy
# We aim to use TPU
try:
    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()  # TPU detection.
    print('Running on TPU ', tpu.master())
except ValueError:
    tpu = None
if tpu:
    tf.config.experimental_connect_to_cluster(tpu)
    tf.tpu.experimental.initialize_tpu_system(tpu)
    strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
    strategy = tf.distribute.get_strategy()

Load the data from GCS bucket:

# Data access ...
GCS_DS_PATH = f"gs://flower-tpu"

# configuration ...
# We have used 224x224 pixels, options are one of either 192x192, 224x224, 331x331, 512x512
IMAGE_SIZE = [224, 224]
                       
EPOCHS = epochs
BATCH_SIZE = 16 * strategy.num_replicas_in_sync

GCS_PATH_SELECT = { # available image sizes
    192: GCS_DS_PATH + '/tfrecords-jpeg-192x192',
    224: GCS_DS_PATH + '/tfrecords-jpeg-224x224',
    331: GCS_DS_PATH + '/tfrecords-jpeg-331x331',
    512: GCS_DS_PATH + '/tfrecords-jpeg-512x512'
}
GCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]

# FileNames ...
TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')
VALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')
TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec')