import re
import signal
-from colorama import Fore, Back, Style
+try:
+ from colorama import Fore, Back, Style
+except ImportError:
+ Fore, Back, Style = '', '', ''
# Pytorch
super(DeepNet2, self).__init__()
self.nb_channels = 512
self.conv1 = nn.Conv2d( 1, 32, kernel_size=7, stride=4, padding=3)
- self.conv2 = nn.Conv2d( 32, nb_channels, kernel_size=5, padding=2)
- self.conv3 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, padding=1)
- self.conv4 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, padding=1)
- self.conv5 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, padding=1)
+ self.conv2 = nn.Conv2d( 32, self.nb_channels, kernel_size=5, padding=2)
+ self.conv3 = nn.Conv2d(self.nb_channels, self.nb_channels, kernel_size=3, padding=1)
+ self.conv4 = nn.Conv2d(self.nb_channels, self.nb_channels, kernel_size=3, padding=1)
+ self.conv5 = nn.Conv2d(self.nb_channels, self.nb_channels, kernel_size=3, padding=1)
self.fc1 = nn.Linear(16 * self.nb_channels, 512)
self.fc2 = nn.Linear(512, 512)
self.fc3 = nn.Linear(512, 2)
else:
validation_set = None
- train_model(model, model_filename, train_set, validation_set, nb_epochs_done = nb_epochs_done)
+ train_model(model, model_filename,
+ train_set, validation_set,
+ nb_epochs_done = nb_epochs_done)
+
log_string('saved_model ' + model_filename)
nb_train_errors = nb_errors(model, train_set)