chjinny

#
#  Example from code built on the Flask web framework (and Werkzeug)
#  Accepts uploading a photo file in the 'photo' form member, then
#  copies it into a memory byte array and converts it to a numpy array
#  which in turn can be decoded by OpenCV.
#
#  Beware that this increases the memory pressure and you should 
#  configure a max request size before doing so.
# 
#  It saves a round-trip to a temporary file, though.

chjinny

model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...

chjinny

text = []
with open("/content/log.csv", "r") as f:
  text = f.read().splitlines() 

logs = text[3:]
loss_train = [[],[]]
loss_valid = [[],[]]


for i in range(len(logs)):

chjinny

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

chjinny

def SPCE(sample_prediction_events, sample_target_events, thresh=0.25):
    diff = np.abs(sample_prediction_events - sample_target_events)
    if np.sum(diff > thresh) > 0:
        ret_spce = 0
    else:
        ret_spce = 1
    return ret_spce

chjinny

from PIL import Image
import base64
import numpy
from io import BytesIO

data_uri = "data:image/png;base64,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