def main():
epochs = 10
batches = 4096
z_k = 1024
iters = 5
outputpath = "output/skipgram_flat-l1"
cooccurrence = np.load('output/cooccurrence.npy').astype(np.float32)
weights = [1e-4, 1e-5, 1e-6, 1e-7, 5e-8, 1e-8, 5e-9, 1e-9]
regularizers = [l1(w) for w in weights]
labels = ["l1-{:.01e}".format(w) for w in weights]
train_flat_regularizer_battery(outputpath=outputpath,
cooccurrence=cooccurrence,
epochs=epochs,
batches=batches,
iters=iters,
z_k=z_k,
labels=labels,
regularizers=regularizers,
is_weight_regularizer=True,
kwdata={'weights': np.array(weights)})
def main():
epochs = 10
batches = 4096
z_k = 256
iters = 1
outputpath = "output/skipgram_256-el"
cooccurrence = np.load('output/cooccurrence.npy').astype(np.float32)
weights = [1e-7, 1e-8, 1e-9, 1e-10, 1e-11, 1e-12, 1e-13]
regularizers = [ExclusiveLasso(w) for w in weights]
labels = ["el-{:.01e}".format(w) for w in weights]
train_flat_regularizer_battery(outputpath=outputpath,
cooccurrence=cooccurrence,
epochs=epochs,
batches=batches,
iters=iters,
z_k=z_k,
labels=labels,
regularizers=regularizers,
is_weight_regularizer=True,
kwdata={'weights': np.array(weights)})
def main():
epochs = 10
batches = 4096
z_k = 1024
iters = 3
outputpath = "output/skipgram_1024_h"
cooccurrence = np.load('output/cooccurrence.npy').astype(np.float32)
weights = [1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 1e-7]
regularizers = [EntropyRegularizer(w) for w in weights]
labels = ["h-{:.01e}".format(w) for w in weights]
train_flat_regularizer_battery(outputpath=outputpath,
cooccurrence=cooccurrence,
epochs=epochs,
batches=batches,
iters=iters,
z_k=z_k,
labels=labels,
regularizers=regularizers,
is_weight_regularizer=False,
kwdata={'weights': np.array(weights)})