Implementing Keras clone with pytorch backend.
pip install keratorch
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