asmith26

{"loss": "categorical_crossentropy", "optimizer": {"nesterov": true, "lr": 0.10000000149011612, "name": "SGD", "momentum": 0.8999999761581421, "decay": 0.0}, "class_name": "Model", "loss_weights": null, "keras_version": "1.0.3", "config": {"layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 3, 32, 32], "name": "input_1", "input_dtype": "float32"}, "inbound_nodes": [], "name": "input_1"}, {"class_name": "Convolution2D", "config": {"W_constraint": null, "b_constraint": null, "name": "convolution2d_1", "activity_regularizer": null, "trainable": true, "dim_ordering": "th", "nb_col": 3, "subsample": [1, 1], "init": "glorot_uniform", "bias": true, "nb_filter": 16, "border_mode": "same", "b_regularizer": null, "W_regularizer": {"l2": 9.999999747378752e-05, "name": "WeightRegularizer", "l1": 0.0}, "activation": "linear", "nb_row": 3}, "inbound_nodes": [[["input_1", 0, 0]]], "name": "convolution2d_1"}, {"class_name": "BatchNormalization", "config": {"name": "batchnormalization_1", "epsilon":

asmith26

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
np.random.seed(2 ** 10)

# Prevent reaching to maximum recursion depth in `theano.tensor.grad`
# import sys
# sys.setrecursionlimit(2 ** 20)

asmith26

$ python create_SDmodel_json_or_yaml.py
Using Theano backend.
/usr/local/lib/python2.7/dist-packages/yaml/representer.py:142: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  if data in [None, ()]:
Traceback (most recent call last):
  File "train.py", line 167, in <module>
    f.write( model.to_yaml() )
  File "/home/andyandy/git/keras/keras/engine/topology.py", line 2407, in to_yaml
    return yaml.dump(self._updated_config(), **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/yaml/__init__.py", line 202, in dump

asmith26

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.

It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data

In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/

asmith26

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.

It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data

In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/