diff --git a/Pilot1/TC1/tc1_pytorch.py b/Pilot1/TC1/tc1_pytorch.py
new file mode 100644
index 00000000..7d94cfeb
--- /dev/null
+++ b/Pilot1/TC1/tc1_pytorch.py
@@ -0,0 +1,194 @@
+#Alex Nicolellis
+from torch.optim import lr_scheduler
+from tc1_baseline_keras2 import initialize_parameters
+import numpy as np
+import os
+import sys
+
+import torch
+from torch import nn , optim
+from torch.utils.data import DataLoader
+
+file_path = os.path.dirname(os.path.realpath(__file__))
+lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common'))
+
+sys.path.append(lib_path2)
+import tc1 as bmk
+import candle
+import pytorch_utils as utils
+
+from timeit import default_timer as timer
+
+gParameters = initialize_parameters()
+
+class myNeuralNetwork(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+        self.dense_first = True
+        self.act = utils.build_activation(gParameters['activation'])
+        if gParameters['dropout']:
+            self.dropout = nn.Dropout(gParameters['dropout'])
+        layer_list = list(range(0, len(gParameters['conv']), 3))
+        self.convs = nn.ModuleList()
+        self.pools = nn.ModuleList()
+        self.linears = nn.ModuleList()
+        for _, i in enumerate(layer_list):
+            out_channels = gParameters['conv'][i]
+            kernel_size = gParameters['conv'][i + 1]
+            stride = gParameters['conv'][i + 2]
+            #print(i / 3, out_channels, kernel_size, stride)
+            if gParameters['pool']:
+                pool_list = gParameters['pool']
+                if type(pool_list) != list:
+                    pool_list = list(pool_list)
+            if out_channels <= 0 or kernel_size <= 0 or stride <= 0:
+                break
+            self.dense_first = False
+            if 'locally_connected' in gParameters:
+                #model.add(LocallyConnected1D(filters, filter_len, strides=stride,
+                                            #padding='valid', input_shape=(x_train_len, 1)))
+                pass
+            else:
+                # input layer
+                if i == 0:
+                    self.convs.append(nn.Conv1d(1, out_channels=out_channels, kernel_size=kernel_size,
+                                    stride=stride))
+                else:
+                    in_channels = gParameters['conv'][i-3]
+                    self.convs.append(nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
+                                    stride=stride))
+            #print(self.act)
+            if gParameters['pool']:
+                self.pools.append(nn.MaxPool1d(kernel_size=pool_list[i // 3]))
+        
+        self.flat = nn.Flatten(start_dim=1)
+
+    def forward(self, x):
+        if not self.dense_first:
+            for i in range(len(self.convs)):
+                x = self.convs[i](x)
+                x = self.act(x)
+                if gParameters['pool']:
+                    x = self.pools[i](x)
+            x = self.flat(x)
+        
+        #should only run once: create linear layers
+        #this is in the forward method because we need access to x.size() to define the first linear layer
+        if len(self.linears) == 0:
+            for i, layer in enumerate(gParameters['dense']):
+                if layer:
+                    if i == 0:
+                        input_shape = x.size()[1]
+                        self.linears.append(nn.Linear(in_features=input_shape, out_features=layer))
+                    else:
+                        self.linears.append(nn.Linear(in_features=gParameters['dense'][i-1], out_features=layer))
+            self.linears.append(nn.Linear(in_features=gParameters['dense'][-1], out_features=gParameters['classes']))
+        
+        for i in range(len(self.linears)-1):
+            x = self.linears[i](x)
+            x = self.act(x)
+            if gParameters['dropout']:
+                x = self.dropout(x)
+
+        if self.dense_first:
+            x = self.flat(x)
+        
+        x = self.linears[-1](x)
+
+        return x
+
+#preprocess data
+X_train, Y_train, X_test, Y_test = bmk.load_data(gParameters)
+
+x_train_len = X_train.shape[1]
+
+X_train = np.expand_dims(X_train, axis=1)
+X_test = np.expand_dims(X_test, axis=1)
+
+output_dir = gParameters['output_dir']
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+model = myNeuralNetwork()
+print(model)
+
+kerasDefaults = {'momentum_sgd': 0.0, 'nesterov_sgd': False, 'rho' : 0.9, 'epsilon' : 1e-07, 
+                'beta_1': 0.9, 'beta_2': 0.999}
+
+# the utils function is outdated
+if gParameters['optimizer'] == 'sgd':
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+else:
+    optimizer = utils.build_optimizer(model, gParameters['optimizer'], 0.01, kerasDefaults)
+
+scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, verbose=True)
+criterion = nn.CrossEntropyLoss()
+epochs = gParameters['epochs']
+batchsize = gParameters['batch_size']
+
+#put data into Dataloaders for easier parsing later
+train_data = []
+for i in range(len(X_train)):
+    train_data.append([X_train[i], Y_train[i]])
+
+trainloader = DataLoader(train_data, batch_size=batchsize)
+
+test_data = []
+for i in range(len(X_test)):
+    test_data.append([X_test[i], Y_test[i]])
+
+testloader = DataLoader(test_data, batch_size=batchsize)
+
+start = timer()
+
+#training
+for epoch in range(epochs):
+    model.train()
+    val_correct = 0
+    val_total = 0
+    correct = 0
+    for i, (inputs, targets) in enumerate(trainloader):
+        optimizer.zero_grad()
+        output = model(inputs)
+        _, predicted = torch.max(output.data, 1)
+        _, label = torch.max(targets.data, 1)
+        loss = criterion(output, label)
+        loss.backward()
+        optimizer.step()
+        correct += (predicted == label).sum().item()
+    
+    #get validation data
+    with torch.no_grad():
+        model.eval()
+        for i, (inputs, targets) in enumerate(testloader):
+            output = model(inputs)
+            _, predicted = torch.max(output.data, 1)
+            _, label = torch.max(targets.data, 1)
+            val_loss = criterion(output, label)
+            val_total += label.size(0)
+            val_correct += (predicted == label).sum().item()
+
+    print('Epoch {}/{}, Loss: {:.3f}, Accuracy: {:.3f}, Val Loss: {:.3f}, Val Accuracy: {:.3f}'.format(epoch+1, 
+            epochs, loss.item(), correct/len(train_data)*100, val_loss.item(), val_correct/val_total*100))
+    scheduler.step(val_loss)
+    
+print('training done')
+
+#testing
+with torch.no_grad():
+    correct = 0
+    total = 0
+    model.eval()
+    for i, (inputs, targets) in enumerate(testloader):
+        output = model(inputs)
+        _, predicted = torch.max(output.data, 1)
+        _, label = torch.max(targets.data, 1)
+        total += label.size(0)
+        correct += (predicted == label).sum().item()
+
+end = timer()
+
+print('Final Accuracy: %.2f %%' % (100 * correct / total))
+print('Final Time: %d seconds' % (end - start))
diff --git a/Pilot1/TC1/tc1_pytorch2.py b/Pilot1/TC1/tc1_pytorch2.py
new file mode 100644
index 00000000..6d123ff6
--- /dev/null
+++ b/Pilot1/TC1/tc1_pytorch2.py
@@ -0,0 +1,160 @@
+from torch.optim import lr_scheduler
+from tc1_baseline_keras2 import initialize_parameters
+import numpy as np
+import os
+import sys
+
+import torch
+from torch import nn , optim
+from torch.utils.data import DataLoader
+
+file_path = os.path.dirname(os.path.realpath(__file__))
+lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common'))
+
+sys.path.append(lib_path2)
+import tc1 as bmk
+import candle
+
+from timeit import default_timer as timer
+
+class myNeuralNetwork(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+        self.act = nn.ReLU()
+
+        self.conv = nn.Conv1d(1, 128, 20)
+        self.pool1 = nn.MaxPool1d(1)
+
+        self.conv2 = nn.Conv1d(128, 128, 10)
+        self.pool2 = nn.MaxPool1d(10)
+
+        self.drop = nn.Dropout(0.1)
+
+        self.lin = nn.Linear(773760, 200)
+        self.lin2 = nn.Linear(200, 20)
+        self.lin3 = nn.Linear(20, 36)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.act(x)
+        x = self.pool1(x)
+        
+        x = self.conv2(x)
+        x = self.act(x)
+        x = self.pool2(x)
+        
+        x = torch.flatten(x, start_dim=1)
+        
+        x = self.lin(x)
+        x = self.act(x)
+        x = self.drop(x)
+        
+        x = self.lin2(x)
+        x = self.act(x)
+        x = self.drop(x)
+        
+        x = self.lin3(x)
+
+        return x
+
+gParameters = initialize_parameters()
+X_train, Y_train, X_test, Y_test = bmk.load_data(gParameters)
+
+x_train_len = X_train.shape[1]
+
+X_train = np.expand_dims(X_train, axis=1)
+X_test = np.expand_dims(X_test, axis=1)
+
+output_dir = gParameters['output_dir']
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+model = myNeuralNetwork()
+print(model)
+
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, verbose=True)
+criterion = nn.CrossEntropyLoss()
+epochs = gParameters['epochs']
+#batchsize = gParameters['batch_size']
+batchsize = 16
+
+train_data = []
+for i in range(len(X_train)):
+    train_data.append([X_train[i], Y_train[i]])
+
+trainloader = DataLoader(train_data, batch_size=batchsize)
+
+test_data = []
+for i in range(len(X_test)):
+    test_data.append([X_test[i], Y_test[i]])
+
+testloader = DataLoader(test_data, batch_size=batchsize)
+
+start = timer()
+
+for epoch in range(epochs):
+    model.train()
+    #running_loss = 0.0
+    correct = 0
+    for i, (inputs, targets) in enumerate(trainloader):
+        #print('t:', targets.shape)
+        optimizer.zero_grad()
+        output = model(inputs)
+        _, predicted = torch.max(output.data, 1)
+        _, label = torch.max(targets.data, 1)
+        #print('o:', output.shape)
+        loss = criterion(output, torch.max(targets, 1)[1])
+        #running_loss += loss.item()
+        loss.backward()
+        optimizer.step()
+        #scheduler.step(running_loss/len(trainloader))
+        correct += (predicted == label).sum().item()
+        #print('Finished Epoch %d Batch %d' % (epoch, i))
+    
+    #output = (output>0.5).float()
+    #correct = (output == targets).float().sum().item()
+    #print(loss, correct, len(train_data), 'should be 4000 ish')
+    with torch.no_grad():
+        val_correct = 0
+        val_total = 0
+        model.eval()
+        for i, (inputs, targets) in enumerate(testloader):
+            output = model(inputs)
+            _, predicted = torch.max(output.data, 1)
+            #print(predicted.shape, targets.shape)#20, 20,36
+            _, label = torch.max(targets.data, 1)
+            val_loss = criterion(output, torch.max(targets, 1)[1])
+            #print(label.shape)
+            #print('predicted:', predicted)
+            #print('targets:', label)
+            val_total += label.size(0)
+            val_correct += (predicted == label).sum().item()
+            #print('Finished Batch %d' % i)
+    print('Epoch {}/{}, Loss: {:.3f}, Accuracy: {:.3f}, Val Loss: {:.3f}, Val Accuracy: {:.3f}'.format(epoch+1, epochs, loss.item(), correct/len(train_data)*100, val_loss.item(), val_correct/val_total*100))
+    scheduler.step(val_loss)
+    
+print('training done')
+
+with torch.no_grad():
+    correct = 0
+    total = 0
+    model.eval()
+    for i, (inputs, targets) in enumerate(testloader):
+        output = model(inputs)
+        _, predicted = torch.max(output.data, 1)
+        #print(predicted.shape, targets.shape)#20, 20,36
+        _, label = torch.max(targets.data, 1)
+        #print(label.shape)
+        #print('predicted:', predicted)
+        #print('targets:', label)
+        total += label.size(0)
+        correct += (predicted == label).sum().item()
+        #print('Finished Batch %d' % i)
+
+end = timer()
+
+print('Final Accuracy: %.2f %%' % (100 * correct / total))
+print('Final Time: %d seconds' % (end - start))