keraTorch 0.0.4

A keras like wrapper for pytorch

KeraTorch

Implementing Keras clone with pytorch backend.

Install

pip install keratorch

How to use

from keraTorch.model import Sequential
from keraTorch.layers import *
from keraTorch.losses import *

The data:

x_train.shape, y_train.shape, x_valid.shape, y_valid.shape

((50000, 784), (50000,), (10000, 784), (10000,))

Model definition:

model = Sequential()
model.add(Dense(100, x_train.shape[1], activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dense(10))
model.add(Activation('softmax'))

Doesn't actually compile anything but to look like keras we specify the loss as below. ce4softmax means crossentropy for softmax loss.

model.compile(ce4softmax)

Burrow for Fastai's learning rate finder to find best learning rate:

bs = 256
model.lr_find(x_train, y_train, bs=bs)

Min numerical gradient: 9.12E-03
Min loss divided by 10: 1.45E-02

We have the same .fit and .predict functions:

model.fit(x_train, y_train, bs, epochs=10, lr=1e-2)

epoch	train_loss	valid_loss	time
0	2.298158	2.270433	00:01
1	2.249195	2.054905	00:01
2	2.082948	1.474771	00:01
3	1.806854	0.904923	00:01
4	1.526004	0.737786	00:01
5	1.293055	0.705958	00:01
6	1.105806	0.666755	00:01
7	0.958004	0.687373	00:01
8	0.838495	0.696255	00:01
9	0.741785	0.697341	00:01

preds = model.predict(x_valid)
accuracy = (preds.argmax(axis=-1) == y_valid).mean()
print(f'Predicted accuracy is {accuracy:.2f}')

Predicted accuracy is 0.81