Chris Hays johnchrishays

	Train BCE	Test classification error
Classifier with face crop	0.693	49%
Classifier with face crop and aggregation	0.693	47%
Classifier without face crop	0.704	51%
Classifier without face crop with aggregation	0.697	50%

	Train MSE	Test MSE
Face cropped CAE (hidden dim = 1296)	0.039	0.045
Un-preprocessed CAE (hidden dim = 3600)	0.014	0.014

	class FaceClassifier(nn.Module):
	def __init__(self, n_vid_features, n_aud_features, n_head, n_layers, n_linear_hidden=30, dropout=0.1):
	super(FaceClassifier, self).__init__()
	# video
	self.vid_pos_encoder = PositionalEncoding(d_model=n_vid_features)
	vid_encoder_layer = nn.TransformerEncoderLayer(d_model=n_vid_features, nhead=n_head)
	self.vid_transformer_encoder = nn.TransformerEncoder(vid_encoder_layer, num_layers=n_layers)
	#self.dropout = nn.Dropout(p=dropout)
	self.vid_pred = nn.Linear(n_vid_features, 1)

	class FaceAutoencoder(nn.Module):
	def __init__(self, n_out_channels1=4, n_out_channels2=4, n_out_channels3=1, \
	kernel_size1=5, kernel_size2=5, kernel_size3=5):
	super(FaceAutoencoder, self).__init__()
	self.encoder = nn.Sequential(
	nn.Conv2d(N_IN_CHANNELS, out_channels=n_out_channels1, kernel_size=kernel_size1, stride=2, padding=2), # [3,160,160] -> [4, 80, 80]
	nn.ReLU(),

	nn.Dropout(0.3),