5-Captcha-Text-Recognition-With-CRNN.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"\n**Project Repository:** https://github.com/GokulKarthik/deep-learning-projects-pytorch"},{"metadata":{},"cell_type":"markdown","source":"### References:\n[1] https://github.com/carnotaur/crnn-tutorial/"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import os\nimport glob\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms\nfrom torchvision.models import resnet18\n\nimport string\nfrom tqdm.notebook import tqdm\nimport cv2\nfrom PIL import Image\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nimport multiprocessing as mp","execution_count":1,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cpu_count = mp.cpu_count()\nprint(cpu_count)","execution_count":2,"outputs":[{"output_type":"stream","text":"2\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## 1. Make train-test split"},{"metadata":{},"cell_type":"markdown","source":"**Data Link**: https://www.kaggle.com/shawon10/captcha-recognition"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"data_path = \"/kaggle/input/captcha-version-2-images/samples\"","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"image_fns = os.listdir(data_path)\nprint(len(image_fns))\nprint(np.unique([len(image_fn.split(\".\")[0]) for image_fn in image_fns]))","execution_count":4,"outputs":[{"output_type":"stream","text":"1071\n[5 7]\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"for idx, image_fn in enumerate(image_fns):\n    if len(image_fn.split(\".\")[0]) != 5:\n           print(idx, image_fn)","execution_count":5,"outputs":[{"output_type":"stream","text":"576 samples\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"image_fns.remove('samples')\nprint(len(image_fns))","execution_count":6,"outputs":[{"output_type":"stream","text":"1070\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"image_fns_train, image_fns_test = train_test_split(image_fns, random_state=0)\nprint(len(image_fns_train), len(image_fns_test))","execution_count":7,"outputs":[{"output_type":"stream","text":"802 268\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## 2. Define character maps"},{"metadata":{"trusted":true},"cell_type":"code","source":"image_ns = [image_fn.split(\".\")[0] for image_fn in image_fns]\nimage_ns = \"\".join(image_ns)\nletters = sorted(list(set(list(image_ns))))\nprint(len(letters))\nprint(letters)","execution_count":8,"outputs":[{"output_type":"stream","text":"19\n['2', '3', '4', '5', '6', '7', '8', 'b', 'c', 'd', 'e', 'f', 'g', 'm', 'n', 'p', 'w', 'x', 'y']\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"vocabulary = [\"-\"] + letters\nprint(len(vocabulary))\nprint(vocabulary)\nidx2char = {k:v for k,v in enumerate(vocabulary, start=0)}\nprint(idx2char)\nchar2idx = {v:k for k,v in idx2char.items()}\nprint(char2idx)","execution_count":9,"outputs":[{"output_type":"stream","text":"20\n['-', '2', '3', '4', '5', '6', '7', '8', 'b', 'c', 'd', 'e', 'f', 'g', 'm', 'n', 'p', 'w', 'x', 'y']\n{0: '-', 1: '2', 2: '3', 3: '4', 4: '5', 5: '6', 6: '7', 7: '8', 8: 'b', 9: 'c', 10: 'd', 11: 'e', 12: 'f', 13: 'g', 14: 'm', 15: 'n', 16: 'p', 17: 'w', 18: 'x', 19: 'y'}\n{'-': 0, '2': 1, '3': 2, '4': 3, '5': 4, '6': 5, '7': 6, '8': 7, 'b': 8, 'c': 9, 'd': 10, 'e': 11, 'f': 12, 'g': 13, 'm': 14, 'n': 15, 'p': 16, 'w': 17, 'x': 18, 'y': 19}\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## 3. Define data loader"},{"metadata":{"trusted":true},"cell_type":"code","source":"batch_size = 16","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class CAPTCHADataset(Dataset):\n    \n    def __init__(self, data_dir, image_fns):\n        self.data_dir = data_dir\n        self.image_fns = image_fns\n        \n    def __len__(self):\n        return len(self.image_fns)\n    \n    def __getitem__(self, index):\n        image_fn = self.image_fns[index]\n        image_fp = os.path.join(self.data_dir, image_fn)\n        image = Image.open(image_fp).convert('RGB')\n        image = self.transform(image)\n        text = image_fn.split(\".\")[0]\n        return image, text\n    \n    def transform(self, image):\n        \n        transform_ops = transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))\n        ])\n        return transform_ops(image)","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"trainset = CAPTCHADataset(data_path, image_fns_train) \ntestset = CAPTCHADataset(data_path, image_fns_test)\ntrain_loader = DataLoader(trainset, batch_size=batch_size, num_workers=cpu_count, shuffle=True)\ntest_loader = DataLoader(testset, batch_size=batch_size, num_workers=cpu_count, shuffle=False)\nprint(len(train_loader), len(test_loader))","execution_count":12,"outputs":[{"output_type":"stream","text":"51 17\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"image_batch, text_batch = iter(train_loader).next()\nprint(image_batch.size(), text_batch)","execution_count":13,"outputs":[{"output_type":"stream","text":"torch.Size([16, 3, 50, 200]) ('nbp3e', 'c86md', '6pfy4', 'e3cfe', '7gce6', 'f858x', 'mcc2x', 'bp2d4', '74853', 'bw44w', 'w6ny4', 'nn6w6', '7cgym', 'n3bm6', 'm3b5p', 'mxyxw')\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## 4. Define model"},{"metadata":{"trusted":true},"cell_type":"code","source":"num_chars = len(char2idx)\nprint(num_chars)\nrnn_hidden_size = 256","execution_count":14,"outputs":[{"output_type":"stream","text":"20\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nprint(device)","execution_count":15,"outputs":[{"output_type":"stream","text":"cuda\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"resnet = resnet18(pretrained=True)\n#print(resnet)","execution_count":16,"outputs":[{"output_type":"stream","text":"Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/checkpoints/resnet18-5c106cde.pth\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=46827520.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a7f7a16e82514db6b874d54938f63733"}},"metadata":{}},{"output_type":"stream","text":"\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"class CRNN(nn.Module):\n    \n    def __init__(self, num_chars, rnn_hidden_size=256, dropout=0.1):\n        \n        super(CRNN, self).__init__()\n        self.num_chars = num_chars\n        self.rnn_hidden_size = rnn_hidden_size\n        self.dropout = dropout\n        \n        # CNN Part 1\n        resnet_modules = list(resnet.children())[:-3]\n        self.cnn_p1 = nn.Sequential(*resnet_modules)\n        \n        # CNN Part 2\n        self.cnn_p2 = nn.Sequential(\n            nn.Conv2d(256, 256, kernel_size=(3,6), stride=1, padding=1),\n            nn.BatchNorm2d(256),\n            nn.ReLU(inplace=True)\n        )\n        self.linear1 = nn.Linear(1024, 256)\n        \n        # RNN\n        self.rnn1 = nn.GRU(input_size=rnn_hidden_size, \n                            hidden_size=rnn_hidden_size,\n                            bidirectional=True, \n                            batch_first=True)\n        self.rnn2 = nn.GRU(input_size=rnn_hidden_size, \n                            hidden_size=rnn_hidden_size,\n                            bidirectional=True, \n                            batch_first=True)\n        self.linear2 = nn.Linear(self.rnn_hidden_size*2, num_chars)\n        \n        \n    def forward(self, batch):\n        \n        batch = self.cnn_p1(batch)\n        # print(batch.size()) # torch.Size([-1, 256, 4, 13])\n        \n        batch = self.cnn_p2(batch) # [batch_size, channels, height, width]\n        # print(batch.size())# torch.Size([-1, 256, 4, 10])\n        \n        batch = batch.permute(0, 3, 1, 2) # [batch_size, width, channels, height]\n        # print(batch.size()) # torch.Size([-1, 10, 256, 4])\n         \n        batch_size = batch.size(0)\n        T = batch.size(1)\n        batch = batch.view(batch_size, T, -1) # [batch_size, T==width, num_features==channels*height]\n        # print(batch.size()) # torch.Size([-1, 10, 1024])\n        \n        batch = self.linear1(batch)\n        # print(batch.size()) # torch.Size([-1, 10, 256])\n        \n        batch, hidden = self.rnn1(batch)\n        feature_size = batch.size(2)\n        batch = batch[:, :, :feature_size//2] + batch[:, :, feature_size//2:]\n        # print(batch.size()) # torch.Size([-1, 10, 256])\n        \n        batch, hidden = self.rnn2(batch)\n        # print(batch.size()) # torch.Size([-1, 10, 512])\n        \n        batch = self.linear2(batch)\n        # print(batch.size()) # torch.Size([-1, 10, 20])\n        \n        batch = batch.permute(1, 0, 2) # [T==10, batch_size, num_classes==num_features]\n        # print(batch.size()) # torch.Size([10, -1, 20])\n        \n        return batch","execution_count":17,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def weights_init(m):\n    classname = m.__class__.__name__\n    if type(m) in [nn.Linear, nn.Conv2d, nn.Conv1d]:\n        torch.nn.init.xavier_uniform_(m.weight)\n        if m.bias is not None:\n            m.bias.data.fill_(0.01)\n    elif classname.find('BatchNorm') != -1:\n        m.weight.data.normal_(1.0, 0.02)\n        m.bias.data.fill_(0)","execution_count":18,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"crnn = CRNN(num_chars, rnn_hidden_size=rnn_hidden_size)\ncrnn.apply(weights_init)\ncrnn = crnn.to(device)","execution_count":19,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#crnn","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"text_batch_logits = crnn(image_batch.to(device))\nprint(text_batch)\nprint(text_batch_logits.shape)","execution_count":21,"outputs":[{"output_type":"stream","text":"('nbp3e', 'c86md', '6pfy4', 'e3cfe', '7gce6', 'f858x', 'mcc2x', 'bp2d4', '74853', 'bw44w', 'w6ny4', 'nn6w6', '7cgym', 'n3bm6', 'm3b5p', 'mxyxw')\ntorch.Size([10, 16, 20])\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## 5. Define loss"},{"metadata":{"trusted":true},"cell_type":"code","source":"criterion = nn.CTCLoss(blank=0)","execution_count":22,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def encode_text_batch(text_batch):\n    \n    text_batch_targets_lens = [len(text) for text in text_batch]\n    text_batch_targets_lens = torch.IntTensor(text_batch_targets_lens)\n    \n    text_batch_concat = \"\".join(text_batch)\n    text_batch_targets = [char2idx[c] for c in text_batch_concat]\n    text_batch_targets = torch.IntTensor(text_batch_targets)\n    \n    return text_batch_targets, text_batch_targets_lens","execution_count":23,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def compute_loss(text_batch, text_batch_logits):\n    \"\"\"\n    text_batch: list of strings of length equal to batch size\n    text_batch_logits: Tensor of size([T, batch_size, num_classes])\n    \"\"\"\n    text_batch_logps = F.log_softmax(text_batch_logits, 2) # [T, batch_size, num_classes]  \n    text_batch_logps_lens = torch.full(size=(text_batch_logps.size(1),), \n                                       fill_value=text_batch_logps.size(0), \n                                       dtype=torch.int32).to(device) # [batch_size] \n    #print(text_batch_logps.shape)\n    #print(text_batch_logps_lens) \n    text_batch_targets, text_batch_targets_lens = encode_text_batch(text_batch)\n    #print(text_batch_targets)\n    #print(text_batch_targets_lens)\n    loss = criterion(text_batch_logps, text_batch_targets, text_batch_logps_lens, text_batch_targets_lens)\n\n    return loss","execution_count":24,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"compute_loss(text_batch, text_batch_logits)","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"tensor(4.5599, device='cuda:0', grad_fn=<MeanBackward0>)"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## 6. Train model"},{"metadata":{"trusted":true},"cell_type":"code","source":"num_epochs = 50\nlr = 0.001\nweight_decay = 1e-3\nclip_norm = 5","execution_count":26,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"optimizer = optim.Adam(crnn.parameters(), lr=lr, weight_decay=weight_decay)\nlr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, verbose=True, patience=5)","execution_count":27,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"crnn = CRNN(num_chars, rnn_hidden_size=rnn_hidden_size)\ncrnn.apply(weights_init)\ncrnn = crnn.to(device)","execution_count":28,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"epoch_losses = []\niteration_losses = []\nnum_updates_epochs = []\nfor epoch in tqdm(range(1, num_epochs+1)):\n    epoch_loss_list = [] \n    num_updates_epoch = 0\n    for image_batch, text_batch in tqdm(train_loader, leave=False):\n        optimizer.zero_grad()\n        text_batch_logits = crnn(image_batch.to(device))\n        loss = compute_loss(text_batch, text_batch_logits)\n        iteration_loss = loss.item()\n\n        if np.isnan(iteration_loss) or np.isinf(iteration_loss):\n            continue\n          \n        num_updates_epoch += 1\n        iteration_losses.append(iteration_loss)\n        epoch_loss_list.append(iteration_loss)\n        loss.backward()\n        nn.utils.clip_grad_norm_(crnn.parameters(), clip_norm)\n        optimizer.step()\n\n    epoch_loss = np.mean(epoch_loss_list)\n    print(\"Epoch:{}    Loss:{}    NumUpdates:{}\".format(epoch, epoch_loss, num_updates_epoch))\n    epoch_losses.append(epoch_loss)\n    num_updates_epochs.append(num_updates_epoch)\n    lr_scheduler.step(epoch_loss)","execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=50.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e8ccee1f5b0f487986e859af7f504369"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:1    Loss:2.9900871351653455    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:2    Loss:2.419498644623102    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:3    Loss:1.9921862167470596    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:4    Loss:1.6599087832020778    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:5    Loss:1.4582493959688674    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:6    Loss:1.3014318709279977    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:7    Loss:1.2249731339660346    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:8    Loss:1.1271169396007763    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:9    Loss:1.0210105472919988    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:10    Loss:0.9753329952557882    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:11    Loss:0.9316047196294747    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:12    Loss:0.8916015344507554    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:13    Loss:0.8738840070425296    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:14    Loss:0.8947969651689717    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:15    Loss:0.8399395264831244    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:16    Loss:0.8126519462641548    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:17    Loss:0.8683857064621121    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:18    Loss:0.8311487120740554    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:19    Loss:0.8097569393176659    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:20    Loss:0.7513260782933703    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:21    Loss:0.736772402828815    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:22    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HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:44    Loss:0.5907929399434257    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:45    Loss:0.567648145498014    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:46    Loss:0.5579055430842381    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:47    Loss:0.5511149986117494    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:48    Loss:0.5489027768957848    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:49    Loss:0.5459187615151498    NumUpdates:51\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"stream","text":"Epoch:50    Loss:0.545354514729743    NumUpdates:51\n\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n\nax1.plot(epoch_losses)\nax1.set_xlabel(\"Epochs\")\nax1.set_ylabel(\"Loss\")\n\nax2.plot(iteration_losses)\nax2.set_xlabel(\"Iterations\")\nax2.set_ylabel(\"Loss\")\n\nplt.show()","execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x360 with 2 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N+KyROQjDEvIRKSLiCwUke3OJeP3u2gjIjLduWR8k4gMMSoeVzq0DwPA7ZOIyKVkAAtFZBOANXDMIfteRJ4WkSucbd4FkCAiGQAeBDDNpFiJyM8Zuaa6CsAfVHWdiLQDsFZE5qvqtjptLgHQ2/kYAeAN53+9IjwkCAlRoSwOS0SNqOomAINdnH+iznEZgF8ZGceXU8/G5DdXGPkRROQDDOshU9UsVV3nPC4CsB2OFUl1TQLwkXN5+UoAsSKSbFRMrrD0BRH5st4d25kdAhF5gVfmkDmrVw8G0OSScadMNE7aDF0ynhwTzjlkROSzYiJCzA6BiLzA8IRMRKIBfAXgAVUtbPiyi7cYsmS8KckxEcjML4VB83CJiDyGpS+IApehCZmIhMCRjH2iql+7aFK7ZNwpBcBhI2NqqFtCJIrKqnC8lMVhici37c7hJuNEgcrIVZYCxwqk7ar6UhPNZgH4tXO15UgABaqaZVRMrqQmRAEA9uWWePNjiYhaLSSIWykRBSoje8hGAbgFwHgR2eB8TBSRqSIy1dlmDoA9ADIAvA3gbgPjcSk1MRIAsD+31NsfTUTklmeurNkggAkZUaAyrOyFqi5FC3cPZwHFe4yKwR0pcZEQYQ8ZEfmumiLWT3y7BbPvO9fkaIjICJau1A84apGdFhPBHjIi8ln5JRUAgK2HCzHh5cUmR0NERrB8QgY4Jvazh4yIfNWJypPbJu04UmRiJERkFCZkALolROEAe8iIyEeN6pVodghEZDAmZABSEyKRW1KBwjKWviAi39OrQ7TZIRCRwZiQwTFkCYC9ZERERGQKJmRwDFkCXGlJRERE5mBChpM9ZFxpSUT+4Aj33yUKOEzIAESGBqNDuzDsO8YeMiLyTV/ddU7t8Z++2mRiJERkBCZkTqkJUewhIyKf1S+5fe1xpb3axEiIyAhMyJxYi4yIfFmQ7eTGJ1XVamIkRGQEtxIyEekpImHO47Eicp+IxBobmnelJkYhu6gcpRVVZodCRB4WCPew4DoJmZ0JGVHAcbeH7CsAdhHpBeBdAN0BfGpYVCbgxH6igOb39zAbEzKigOZuQlatqlUArgLwsqr+HkCycWF5X6qz9AUTMqKAFFD3sOxCrrIkCjTuJmSVInIDgFsBfO88F2JMSOboWttDxnlkRAEooO5hWUzIiAKOuwnZ7QDOBvA3Vd0rIt0BfGxcWN7XPjwE8VGh2MceMqJA1Kp7mIh0EZGFIrJdRLaKyP0u2owVkQIR2eB8PGFg/PUoRyyJAk6wO41UdRuA+wBAROIAtFPV540MzAzdEiLZQ0YUgNpwD6sC8AdVXSci7QCsFZH5zuvUtURVLzMmaiKyEndXWS4SkfYiEg9gI4D3ReQlY0PzPtYiIwpMrb2HqWqWqq5zHhcB2A6gs3eiJSIrcnfIMkZVCwFcDeB9VR0K4ALjwjJHt4RIHC44gbJKu9mhEJFntfkeJiKpAAYDWOXi5bNFZKOIzBWR/p4K1h1bDxd48+OIyGDuJmTBIpIM4FqcnBAbcFIToqAKZOazl4wowLTpHiYi0XCUzHjAmdDVtQ5AN1U9C8ArAGY2c50pIpIuIuk5OTmtj97pvvN71x5fOn1pm69DRL7H3YTsaQDzAOxW1TUi0gPALuPCMkdNLbJ9x5iQEQWYVt/DRCQEjmTsE1X9uuHrqlqoqsXO4zkAQkQk0dW1VHWGqqapalpSUlKbf4i6xWGJKLC4O6n/vwD+W+f5HgDXGBWUWWpqkXELJaLA0tp7mIgIHAVkt6uqy7lmItIJwFFVVREZDscX3FyPBt5AUIOErNJejZAg7oBHFAjcndSfIiLfiEi2iBwVka9EJMXo4LwtNjIE7cODObGfKMC04R42CsAtAMbXKWsxUUSmishUZ5vJALaIyEYA0wFcr2psQYpr07rUez5/21EjP46IvMitHjIA78OxzcivnM9vdp670IigzCIiSE2Mwv48JmREAaZV9zBVXQqg2fFBVX0VwKsejLFFSe3CsOUvF2PAk/MAcJNxokDibl93kqq+r6pVzscHANo+EcKHdY1nLTKiABQw97DosJPfo6vs1SZGQkSe5G5CdkxEbhaRIOfjZhg8V8IsqQlRyMw/gUre6IgCSUDew4rKqswOgYg8xN2E7DdwLBc/AiALjrkTtxsVlJm6JUTCXq04lH/C7FCIyHMC8h725KytZodARB7iVkKmqgdU9QpVTVLVDqp6JRwFFgNOaiJXWhIFGivdw4jIP53KeukHPRaFD6mpRcaVlkQBz2/vYS9fN6j2mIWsiQLDqSRkAVmhMCk6DJGhQdh7jD1kRAHOb+9hdSf2j/vHIvMCISKPOZWELCDXW4sI+iW3x4aDx80OhYiM5bf3sOE94muPK+2KIwVlJkZDRJ7QbEImIkUiUujiUQTgNC/F6HVpqfHYeriAm4wT+blAvYe1Dw/BsNS42ucbDuabGA0ReUKzCZmqtlPV9i4e7VTV3aKyfietWxwq7YqN7CUj8muBfA978vL+tcfG7g9ARN7ATdBcGNrN8c0zfT+/dRKRb4oIDao9ZsF+Iv/HhMyFuKhQ9EyKwlomZETkoyJCTiZk6r/T4YjIiQlZE9K6xWPt/nxU86snEfmgqNCTI668TRH5PyZkTRiaGoeCE5XYnVNsdihERI3ERIbUHisnkRH5PSZkTUjjPDIi8hPMx4j8HxOyJnRPjEJCVCjS9zEhIyLfxhI9RP6PCVkTRARDusVh7f48s0MhImrWtK83mx0CEZ0iJmTNSOsWh325pcgpKjc7FCKiRp64rF/t8WMzmZQR+TMmZM1Ic1bCZvkLIvJFneMiao8/XnnAxEiI6FQxIWvGgM4xCA22IX0fhy2JyPckRIU2OldQWsk5ZUR+yLCETETeE5FsEdnSxOtjRaRARDY4H08YFUtbhQUHYWDnGK60JCKflJYa3+jcWU//D9fNWGlCNER0KozsIfsAwIQW2ixR1UHOx9MGxtJmQ1PjuNE4EfkV7sNL5H8MS8hUdTEAvx/rS+sWz43GichnvXdbWu1xaUWViZEQ0akwew7Z2SKyUUTmikj/phqJyBQRSReR9JycHG/Gx43GiSxIRLqIyEIR2S4iW0XkfhdtRESmi0iGiGwSkSFmxGqvPnn80JebzAiBiDzAzIRsHYBuqnoWgFcAzGyqoarOUNU0VU1LSkryWoAAEB8Vih7caJzIaqoA/EFV+wIYCeAeEenXoM0lAHo7H1MAvOHdEB2q65Tp355VaEYIROQBpiVkqlqoqsXO4zkAQkQk0ax4mjOMG40TWYqqZqnqOudxEYDtADo3aDYJwEfqsBJArIgkezlU7mNJFCBMS8hEpJOIiPN4uDOWXLPiaQ43GieyLhFJBTAYwKoGL3UGcLDO80w0TtpqrmHYtIv2ESc3Gd+TU+LRaxOR9wQbdWER+QzAWACJIpIJ4EkAIQCgqm8CmAzgLhGpAnACwPXqo1/1RnR3LC1flnEMvTu2MzkaIvIWEYkG8BWAB1S14XiguHiLy3uYqs4AMAMA0tLSPHqfO6enTw4sEFErGZaQqeoNLbz+KoBXjfp8T+qWEIUeSVH4aUc2bhvV3exwiMgLRCQEjmTsE1X92kWTTABd6jxPAXDYG7ERUeAxe5Wl3zj/jA5YtScPxeVcVk4U6JzTKd4FsF1VX2qi2SwAv3authwJoEBVs7wWJBEFFCZkbhp/RkdU2KuxdNcxs0MhIuONAnALgPF1dhOZKCJTRWSqs80cAHsAZAB4G8DdJsWK343vZdZHE5GHGDZkGWjSUuPQLjwYC3YcxYQBncwOh4gMpKpL4XqOWN02CuAe70TUPOf6KCLyY+whc1NIkA1j+3TAgh05LH9BREREHsWErBXOP6MDjhWXY/OhArNDISI6yTcXqBNRKzAha4XzTk+CTYCfdmSbHQoRUS122hP5PyZkrRAXFYqh3eKwYMdRs0MhIqqlrsufEZEfYULWSuPP6IgthwpxpKDM7FCIiAAAt56dir7J7eudyyo4YVI0RNQWTMha6fy+HQAACzhsSUQ+okP7cHxz9zn1zl3z+nIAQGlFFV5bmIEqe7UZoRGRm5iQtVLvDtFIiYvgsCUR+ZQgW/3SF4edvfj/+nEXXpy3E1+vP2RGWETkJiZkrSQiOP+MDliacQxllXazwyEiAgAE21zXIiupcOwuUs77FZFPY0LWBuP7dkRZZTVW7M41OxQiIgCOL4uf/nZE4/PO+rZciUnk25iQtcGI7vGIDA3CTxy2JCIfck7PxHrPK+3VYBF/Iv/AhKwNwkOCMLpXIn7ang07v3YSkQ95/LJ+tcev/LSrdv8nZfFYIp/GhKyNrh6SgqyCMny8cr/ZoRAR1YoMDao9nr4go3afS353JPJtTMja6OL+HXFu70T8Y95OZBexJhkR+YaGc/vLq1jugsgfMCFrIxHBX67oj/Kqajw7e7vZ4RARAUBtj1iNz1YfAADW8ifycUzITkGPpGhMPa8HZm44jOW7j5kdDhERhnaLc3l+2+FCL0dCRK3BhOwU3T2uF7rER+CJb7eigkMDRGSynknRLs9/tS7Ty5EQUWswITtF4SFBeOry/sjILsa7S/eaHQ4REb7/3WizQyCiVmJC5gHn9+2Ii/p1xPSfdiEzv9TscIiIiMjPMCHzkCcud9T+eW7ODpMjISIiIn/DhMxDUuIi8ZvRqZizJQsZ2cVmh0NERER+hAmZB/1mVHeEBdvw5s+7zQ6FiIiI/AgTMg9KiA7D9cO6Yub6Q5xLRkSmaWqXpD99uQnj/rGIJTCIfBATMg+bMqYHAODtxXtMjoSIToWIvCci2SKypYnXx4pIgYhscD6e8HaMTTktNhwAcHrH+iUwvkg/iL3HSjBx+hIzwiKiZjAh87DTYiNw9ZDO+HzNQeQUlZsdDhG13QcAJrTQZomqDnI+nvZCTG5JiA7Drr9dgnkPjGnzNcoq7ZwPS+RFTMgMMPW8nqiwV+P9ZaxLRuSvVHUxgDyz42irkCBbo22UWuOP/92IC176GUVllR6MioiawoTMAD2SojHxzGT8e8V+FJzgzYwogJ0tIhtFZK6I9G+qkYhMEZF0EUnPycnxZnxN2nespNnXV+7JBQCUVXIHEiJvYEJmkLvH9kRReRU+Xrnf7FCIyBjrAHRT1bMAvAJgZlMNVXWGqqapalpSUpLXAgSARX8c6/L82H8s8mocRNQ8JmQG6X9aDMb1ScK7S/fiRIXd7HCIyMNUtVBVi53HcwCEiEiiyWE1kpoYZXYIROQGJmQGumdcL+SVVOA/6QfNDoWIPExEOolzkpaIDIfjfpprblStU1JeZXYIROTEhMxAaanxGNw1Fu8v24vq6iYKAxGRTxKRzwCsANBHRDJF5A4RmSoiU51NJgPYIiIbAUwHcL1qUxXAzPV/vzrL5fn+T87DtW+tAACoKu9TRCZiQmawO0Z3x77cUizYkW12KETUCqp6g6omq2qIqqao6ruq+qaqvul8/VVV7a+qZ6nqSFVdbnbMTblmaEqTr63e61hI+vqi3ejx5zkoZq8ZkSmYkBlsQv9OOC0mHO+xBAYRmej1m4Y0+dq6A/m1C5DySyoAAHZnb5mCvWZE3sCEzGDBQTb8+pxULN+di+1Z3K6EiMwx8cxkDOka6/K1q19fjqyCsnrn8ksdJXtmrj9keGxExITMK24Y1hURIUF4byl7yYjIPF/fParV78ktrjAgEiJqiAmZF8REhmDy0BR8u+EwjhVzOyUiMs+CP5xndghE5AITMi+5bVQqKuzV+GTlAbNDISIL65EUjYEpMWaHQUQNMCHzkp5J0RjXJwn/Xrkf5VUsFEtE5hmWGt/kax+t2Idnvt/m9rWq7NxaicgTmJB50R2je+BYcTm+25hldihEZGHNVUt7e8levFtnvuveZva8XH8gH70enYulu455MjwiSzIsIROR90QkW0S2NPG6iMh0EckQkU0i0vSa7AAxqlcCTu8YjXeX7oWP1o8kIgsIDhK32/5v29EmX1vlrGG2eJdvbJhO5M+M7CH7AMCEZl6/BEBv52MKgDcMjMUniAh+e24PbM8qxFOztjIpIyJTXDmos0euU3MLcz+9I6KmGJaQqepiAHnNNJkE4CN1WAkgVkSSjYrHV0wemoLfntsdH67Yj0dnbuHx4oZnAAAgAElEQVRWJUTkdf1Oa499z1/a6vcdzCtFbp2V4rVFY5mREZ2yYBM/uzOAurtuZzrPNZpgJSJT4OhFQ9euXb0SnFFEBH+e2BfBQTa8sWg37HbFc1efCZuNdzQi8q6IkCCcqGx5kVFFVTVCg20494WFEAH2PudI5k72kPH+RXSqzJzU7+pvsMvuIlWdoappqpqWlJRkcFjGExE8fHEf3De+F75IP4iHvtxUu00JEZG3XNS/o1vtPlqxr/bY1UwL8VI+VlZpR3ZRWcsNifyQmQlZJoAudZ6nADhsUixeJyJ48KI+ePDC0/HVukw8+J8NqOTycSLyouevHuhWu7/N2e7yfM08WG/1j9363moM/9tPXvo0Iu8yMyGbBeDXztWWIwEUqKrl6kHcd35v/GnCGfh2w2Hc9fE6lLkxfEBE5AkRoUG1xzeNaHo6iCqwak+uy/OA93rIalZ1EgUiI8tefAZgBYA+IpIpIneIyFQRmepsMgfAHgAZAN4GcLdRsfi6u8b2xDOT+uPH7Udx+/trUFxeZXZIRGQxf7vqzGYn+t/y7upG52pGLzmHjOjUGTapX1VvaOF1BXCPUZ/vb245OxXR4cH443834eZ3VuGD24chNjLU7LCIKMB9eucIhIUEtdiuwsWUipoessz8UsxcfwhXDvZMOQ0iK2Klfh9y1eAUvHHTEGw7XIjr3lqJ7EJOXiUiY53TKxFDu8XVPu/QLqzF96ROm42FO7Jry17M3HAYD3yxwbAYiayACZmPuah/J7x/+zAczC/FVa8vx84jRWaHREQWYnNzQtjtH6xBw8XhLHZN1HZMyHzQqF6J+GLK2ai0V2PyG8uxhNuSEJEvapCAsXoPUdsxIfNRZ6bEYOY9o9A5LgK3vb8Gn68+YHZIRGQB485wv9Zjhb1hQsaMjKitmJD5sNNiI/DfqWdjdK9ETPt6M56bu73ZshjZhWX414+7sGBH05sBExE15+lJA7Bs2ng8dmlfAMAzk/o32fbNn3fXe77h4HEOW/qJ2ZuysPHgcbPDoDqYkPm4duEhePfWNNw0oive+nkPhv3tRzz6zWZsrHPj23GkEH/870aM+vsC/PPHXzD13+uwZh/r9RCdChF5T0SyRWRLE6+LiEwXkQwR2SQiQ7wdoxFCgmzoHBuBO8/tgX3PX4pbzk51+72/enMFvt1gmfrebll/IB/XvbUCFVW+Vfj7nk/XYdJryzBz/SGzQyEnJmR+IDjIhr9eOQCf/nYELujbEV+ty8Sk15bh4pcX46Z3VmLCy0swe1MWbhjeFd86hzmnfJSO/bklZodO5M8+ADChmdcvAdDb+ZgC4A0vxGSKv145wO22u3OKDYzE/0z7ajNW7c1r9Hspr7L7RJLG1bG+gwmZnxARnNMzEf+8bhBWP3oBnr3qTESFBWPfsVI8dHEfrHhkPJ6eNABndYnFe7cNgwL4zQdrUHCissVr26sV9322Hre8uwo5ReXG/zBEfkBVFwNorqt5EoCP1GElgFgRSfZOdN41eWiK221fWZCB1Gmz8c6SPQCAuZuzkDptNnYcKTQqPJ9Ws2h11sbDWHcgv/Z8n8d+wLh/LPJqLLtzijF/m29Oaam0V+O7jYeROm02SiusWRydCZkfah8eghtHdMU3d4/Csmnjcc+4XvWKyHZPjMKbNw/FgbxS3P3J2hb3yHx+7nbM2ngYK/fk4vJXlmID5xU069DxE3hnyR5Uc0mZ1XUGcLDO80znuYATHhKEP1x4eqve89fZ2/HoN5vx3rK9AIAJLy8xIjS/8cai3bj69eX1zh06fsKrMZz/fz/jtx+lY6WLbbDM1v/JefjdZ+sBAJn53v29+AomZAFqZI8EPHvVmViWkYsnvt3a5ETbL9YcwNtL9uK2c1Lx7T2jERwkuPbNFfjPmoMu23uTquLdpXvx6Debcc8n63DDjJWY8PJiTHp1qVs9f0b5cPk+/HX2dnzuA78jMpWrgl0u/6KJyBQRSReR9Jwc/yxj87vze7f6PZ+sOoA1+072Ctlb8SXmSEEZUqfNxuJf/PP3VWOHD9aSvH7GSrNDaKTu8K1VV+syIQtgv0rrgrvH9sRnqw9g6sdrcTCvtN7rK/fk4rGZW3Bu70Q8dmlf9DutPb67dzSGd4/Hw19twuMzt5g6x2HNvnw88/02fL8pCzuOFKLSXo1OMeHYmFmAr9dlmhZXzSbLz8/djuwi7qZgYZkAutR5ngLA5Yx2VZ2hqmmqmpaU5H5ZCV8z9/5zcVZKTJvfvyzjWL3nq/fmId25AKm4vAqp02Zj7IsLsXZ/PpbvdrT9eOX+Rtdx9QXz89UHkDptNh78j+/MiSrhvsRtYtF8jAlZoPvjRX3w0MV9sPiXY7jgpZ/x0vxfcKLCjv25Jbjr47XoEh+JV28cguAgxx+FuKhQfHD7MPy/MT3w75X7MeaFhXh78R4UlXm/R+qrtZmIDA3C8mnj8dMfxuLLu87BB7cPx1kpMfh01QFTltcXl1dhy+FCXDowGWWV1Xjm++1ej4F8xiwAv3authwJoEBVs8wOykh9k9vj23tHY2SP+Da9/9fvrcazcxx/ZzLzS3HtWysw+c0VmPzGcrzlLKGxL7cU17yxHA/+ZyMA112Orv7qT/t6MwDg63WHcOZT83C8tKJNMXrCqj25KDhRWW/OGDWt4Zwxq/aQGba5OPkGm01wz7heuHpIZzw3Zwem/7QLX6YfRFhIEKoVeO/WYYiJCKn3nuAgGx6Z2Bfn9k7C64sy8Lc52zH9p124cWRX/GZUd3RsH2543Ccq7Ji9OQuXDEhGVFj9P6Y3juiKP321Gen78zEstW3/MLTV2v35sFcrrh/WBb07ROPlH3fhmiGdMbZPB6/GQcYTkc8AjAWQKCKZAJ4EEAIAqvomgDkAJgLIAFAK4HZzIvW+T+4ciZ5/ntOm985YvAczFu+pdy59fz7S97tOXlwlVg3/uW44F6uorAqDnp6PpX8ah5S4yDbF2ValFVW4bsZKDE+N54pTN2Tml2L03xfWO2fRfIw9ZFaRHBOB6TcMxhdTRqJ9RAgy80vxxs1DkJoY1eR7RvdOxKe/HYlZ947CmD5JeHvxHox5YSFW7zW+xtn/th1BcXkVrhnaeI705WedhnZhwfhslfd3L1i9NxfBNsHQbnG4a2xP9EiKwuPfbsGJiqYL9pJ/UtUbVDVZVUNUNUVV31XVN53JGJyrK+9R1Z6qeqaqppsds7cE2dzb79IT1uzLbzTU2bB3fNTzC1y+9/JXlqLKXo0Plu3Fc3O3Y87mLHy30dg6aZXO3Qs2HypAbknjZPKjFfsM/Xx/cyC3tNE5q/aQMSGzmBE9EjD7vnOx8pHzcU7PRLfeMzAlFq/dOASL/jgOnWMjcPcna5FVYOwqmC/XZqJzbARGdk9o9FpkaDCuHNwZ32/O8vqwxKo9eRjQOQaRocEICw7Cs1ediYN5J/Cvn3Z5NQ4is3VsH1Z7fOnAZCRGhzbT+tTc51x9V2PNvvzauZzN3QPySyvx0Jeb8NR32/DWz3tw9yfralfyGe1EE7uqPPHtVszeFNAj263iKrnfmFlgQiTmY0JmQUE2QUJ0WMsNG+iaEIm3bhmKExV2TP14HcqrjOkVOlJQhmUZx3D1kM6wNfFN/IbhXVFRVY2v1nmvynRZpR0bM49jRJ35MyN7JODatBS8vWQPtmeZW2dp7uYsvL4oA9sOF3L7GjLcyB6OL0v3jOuJ6dcPRvpjF+Khi/sAAJ65cgAuHei5kmy5JRXo8cjs2uc3vL0S181YCVXF9qzmVzF+Y3Al+p1HirB017GWG9Zxz6frDIrm1BzMK0VBqXfnC9fMX67r8ZkuN8cIeEzIqFV6d2yH/7v2LGw8eBxPzGy6nMap+Gb9IVQrcPWQpotR9jutPQZ1icWnq/Z7LflYdyAflXbFiO715609cklfxESE4C/fbfVKHK5U2qsx7evNeOGHnZg4fQnOfm4BHvl6E+ZvO8rkjAxhc1Y87ZEYXdvLcc+4Xtj97ETcMrIbXrtxiEeHNl1VzOj+SNvmsXnSxS8vxs3vrjI7DI8494WFuOjln736mcFeHP72dUzIqNUmDEjGPeN64ov0g/h0tWfncakqvlx7EEO7xaF7M/PbAMfk/t05JfXqHBlp9d48iABpDRYSxEWF4q7zemLlnjxsPWxOV/vqvXkoOFGJZ686Ey9MHojBXWPx3cYs/PajdLy7dK8pMVFgu3tsT3SNj8S4M+ovaKmbhE08s3W9ZL8+u1ur47jh7dbX1HrgcwOHLVv5/Se/pAI/eqF6/kvzf3Gr3dFC7+7W8sPWIy7PX/naMlPLG5mBCRm1yYMX9sF5pyfhqVlbsXa/5yb5b8wswO6cElzTTO9YjcsHnoZ24cH4dFXjOkVGWLUnD/2S26N9eEij165N64KIkCB8sGyfV2JpaN7WIwgPseGqwZ1xbVoXvHHzUKx7/EIMS43DRyv2c1cB8rjeHdth8cPjEB/V9NyxFycPrPd83gNjmr3mpEGdse7xCz0SX3NmbjjcqLj0npzi2u2eToW6yMjqzrdr6Lb3V+POj9KRW2xcIrQ7pxjTfXSe68Id2S7Pbzh4vLb0iVUwIaM2CbIJpl8/GMkxEbjurZW46Z2V+HD5Phw+xa1AvlqbidBgm1vzTyJCg3DV4M6Ys+UI8l2sZvKkiqpqrDuQjxEuFhkAQExkCK4e0hnfbjxs6I3Vlepqxf+2HsV5pychIjSo9nxosA23nJ2KA3mlWJLRujkuRJ4QHhKEK846rfZ5n07tmm3fLjy42QTPk95b6lh5WTOkf+1bK/HX2dux62jRKRV0dTVD4LFL++GD24e5bF8zgb3cwCLcLe1RPODJea3aRcGTrLqi0hUmZNRmMZEh+GzKSEwZ0wNHC8vx5KytOOf5Bbj8laV4cd4O/PxLDopbcWMrr7Jj1sbDuKhfx0a10Zpy44iayf3Gdm1vyjyO8qpqDO/edN2z285JRUVVtde3VNp0qABHCstwcf9OjV67uH9HJESF4hMX1c6JvOGf1w1CeIgNt52TCgB46dqzGrUJD7Ghc2wEeneIBgA8dXk/w+P610+78NbPe9D9kTl4bWEGjjm/SF34z8UY62LT722HC5GRXYwJLy/G/tySJq/rKsGIDA1qsVahNxOTTU9dhM6xEbXPi8urXO6I4A1V9uZ/7rJKu+kLpryFhWHplHSOjcDDE87AwxPOwO6cYszfdhTztx3Fmz/vwWsLd8MmQP/TYjCoSywq7dXILalAXkkF8ksqUK2Ks7rEYmi3OAzpGod9uSUoOFGJa4a2PFxZ44xO7TG0WxzeWbIXVw3u3KbVo+5Y5ay91lxC1rtjO5zbOxH/XrEfU8b0QIiL1UNG+GHLEQTbBOef0bHRa2HBQbh2WBfMWLwHWQUnkBwT4eIKRMYJsgl2PHNJ7fNLBiTXDkUtfmgc4qJC0K7BNIDbRnXH64t2I7uFnp26/nX9INz/uWPbpDduGoK7PnF/JeOL83bWe55TVI6PVjj2rP1q6jnoHBeBidNPbo5+3ouLsP7xC7H1cP1E4aX5v+CVBY2HBsc759ndMrIb/t1E4lNt4C51DafNR4YEYVDX2HoFdZ+cZc6ipMoWfvAzHv8BAPDmzUMxYUDjL53elldSgdF/X4BHL+2Lm0a0fs5jc5iQkcf0TIpGz/OiMfW8nigpr8L6A8exel8eVu/NxTfrDyEiNAgJUaGIjwpF39Paw25XrNidi283nCzU2KFdGM7t5V59tBpPXd4f17y5HL/7bD0++s1wl8uoT9WqvXk4vWN0i8Mpt52Tijs+TMcPW47g8jpDNUZRVfxv6xGM7JGAmEjXvYo3DOuKN3/ejc9XH8TvLzzd8JiImhMRGoQ9z07EsZJydGjX9K4fy6aNhypw0zsr3Vq4M2lQZ8RHhaJDu3D0SIrCJQM6Ye4W1xPG3fHEt44E5fJXl7p8ffAz8+s9f2n+Ly7naQ3vHg9xrkh95soBiI8KdVm3cMyLC/Hjg2PQq0Pzw7qeEBxka5SkuVJRVY0v12bi+mFdmixB1Faqih+3Z9fbL/msLrHYePC4y/a7jhb5REJWrYrSCrshQ7xMyMgQUWHBGN07EaN7N59cqSoOHT+Btfvzsf7AcYzsEd/qhOrMlBj87coBeOjLTXhh3k78eWLfUwm9kSp7Ndbuy2u2DEeNcX06oFtCJD5Yvs8rCVlGdjH2HCvB7aO7N9mma0IkxvROwudrDuB343sZkrAStYbNJs0mYwBqe5g//M1wbM8qcva878a5vROx/sBxl9Mhzu19cuP2N24eiie+3YLuiVH4y3fbPPsDuNDUpPmrBtffbeT+83s3WUh60qvLsOHJi7zSuz7m9CR830KB2jd/3o2X5v+CsGBbq0Yu3DF3yxHc3aAX8/mrz8SSXTl4ds6ORu3tPjLXrCaMmiTbk3hnJlOJCFLiIjFpUGc8dUV/TBjQtmKSv0rrgltGdsOMxXs8vjXK1sOFKKmw1ysI2xSbTXDr2alYuz8fmzJdf9PzpHnOJeMX9Ws8XFnXTSO64mhhOX5qYkUTka+KDA3G0G5xmHbJGdj97ER89Jvh2PKXi91679OTBuD2Ud2x7/lLMfW8ngZH6lrDjqXmeppKKux45adduOfTdTj7uZ8MjetXQ1OwvokVrZ+uOgBVrS2V0Zq5wO7KLixrdK57YhSmjOmJG0d0bfSarywUr1kEYkT1NCZkFDAev6wf0rrF4eEvN2HHEc9NAl3txvyxuianpSAqNAgfLN/nsRiaMm/rUQzuGtvihu/jz+iA5Jhw0ybuEnlCkE1qeyb+NOEM9EiMwsI/jsXqR89v8b0PX9wHm5+6CPuevxR3NNOj7GlJ7RrPa933/KXY9/ylLttPX5CB2ZuykFVQhtRps1Flr8Ynq/bjtYUZLos8L9nlWDz1+eoD9VZ47zxShI9X7kd5lb1eMtM13rHZuoggLioUF/Rt/GXuz99srnf/enLWVqROm42M7Ma7IpyosCN12mx8sKx19Q4b/iS3j0pFeIhjlXiIi6R10U7f+DJZ87u0sYeMqGmhwTa8ftMQtAsPxv/791qPlcJYtTcXPRKjWhxiqdE+PASTh6bg+41ZLS43PxWHjp/A5kMFLldXNhQcZMP1w7piya5jza4QI/IXd43tiQV/HIvubv7dtNmkdvFAzRZPAPDYpZ6d4lDXwJQYjGtmdaU7n93r0bl49JsteHHeTvw3/eRq8oU7s7FkVw5ueXc1Bjw5D9O+3owb316FA7mlKKu04+KXF+OxmVvQ57EfUGE/OU9r8cPj6l3/jZuHuPxcV8O8f3BRFyzPuZfoW4v3YNvhQtz18doWfyZX1w+qk+D8sc7/nxqbMgu8vnexKzV15ozYYIAJGQWUDu3D8cbNQ3D4+Amc8/wC3P/5eizcmY0qe9uWMJVV2rF6b57bvWM1bj0nFZXV1fj1e6uxYndumz67JfOcE5bdScgA4LphXRBkE4/vrkDkb8KCHf/03TW2J+48twcW/OE8Qz5n1r2jm51rdL6L3qnmPPzVJry/bC/W7s/H7e+vwS3vrq73+s6jRRjz4kI89OWmeueX7266DmFIkK3J3rqGKuyKiqpq/N//djaq1Xai0o6J05dg7pYjmLM5C/9JP4jffLCmXpsqezVSp83GnR+mN7p2Wmpc7XG78BDMue/cRm2ue2ul6TuPVNfOIfP8tcXf9rlLS0vT9PTG/zOJ6tpyqACfrT6A7zdloeBEJRKjQ3HZwNMwskc8BqbEIjkmvMkbZWlFFRbtzMGczVlYsCMbpRV2vHXLULcTnxpzN2fhme+34XBBGS7q1xF/ntgXqS1sB9Ua1721AvmlFfjf793/x2Tqv9di9b48LPvT+HpFZH2diKxV1TSz4/AE3sN801OztiIz/wSevXoAznluAc7plYjFv+Q0+5724cG4ZmgKusVH4ikXPUruJDpfr8v0WkX6xy/r1+Rw7cs//oKXf2y5mn+vDtHIyC7GiO7x2J1TjGPFzfdafXXX2ejQLhx5JRWY9NqyJtu5+l0tyziGm95pvE9o3bbHistxtLAM/U+LaTF2T8jML8Xovy/EC5MH4tq0Lm69x937F1dZUkAa0DkGf7vqTDxxeT8s2pmDb9YdwqerDtTOi0hqF4azUmLRLSESpRV2lFZUoaS8CoVlVdiUeRxlldVIiArFlYM747Izk3FOK0txAMAlZyZj3Bkd8M6SPXh90W5c+M+f8euzUzH1vJ4u55W0Rm5xOdbsy8O943q16n13ntsdP2w9gufmbsfTkwacUgxEgeSpK/rXHmc8OxGA48vZuv3HMbRbHP7xv524d1wv/Cf9IIJsgjvP7VHv/eEhQcgrrcALPzhqmv154hlufe7VQ1IQGRqMqW4O9Z2Ky5rZAeX+83vjhy1HsONI43lidWVkFwM4WZuxJde8scL9ABsY1cR9N3XabKx59AIktQvDxH8tQXZReaOErrpaPV6qA6izytLjV2ZCRgEuLDgIF/fvhIv7d0J5lR3bs4qw8eBxbDx4HBsyj2P57mOIDA1GdFgQIkODERUWhF8N7YKJZyZjePf4ehslt0V4SBDuHd8b16Z1wT/+txPvLduLj1fux7VpXTBlTA90cU6wBQB7tWLLoQIs352LkCBB3+T26NOpHRKdxW6rqxUZOcVYtz8f87cdRbUCF7Wy1y4tNR53jO6Od5fuxfl9O+K805NafhORRUWGBteW7nn8MsfuAf+vidWa1w93rAwc0T0BQ7rGtqosQkurpD3F1T68NUQEz159Jq5+fblXYnHXsmnjMer5BY3OX/PGcvzx4j61xYOn/7QLU8/ridBgGw7mleLcFxbipWvPcqtcUVOyi8pgE6m9BwMnEzIjJvUzISPLCAsOwqAusRjUJdbrn92hfThemHwW/t95PTHj5z34fM0BfLr6AC4fmIxh3eOxfHculmUcw/HSykbvTYwOQ9f4COzKLkZRmWPeRmxkCG4e2RX9T2vf6lgeurgPFv+Sg4f+uxHzHhiDOC/tHUhkBUO7xbXcqAGbTfDniWcgNiIUz83djnwX94G2+PmhsVh/4Dge+MKxg0F4SPPTxod0jcOuv12C3o/O9cjnu+OBC3rjnJ5Nj0B0jo3A7PtG49Lp9Qv0HsgrxX2fra99/tL8X2AT4N7xvWt78b7dcPiUErLhf3OUHqnb+1azxZURc8iYkBF5Uc+kaPx98kA8cGFvvLNkLz5bfQAzNxxGx/ZhuKBvR5zbOxGjeiVC1bFsfceRQuw8UoT9eaW4bOBpzm2mYtE9MarNhQnDQ4Lwz+sG4arXl+Gxb7fg1RsGG1LkkIjcN2WMo+dt8tAUvL98H575fhs+uH0YZq4/hPUHj+OaISm1dcHc1S0hqt52cu78PQ8JsiE8xIayymqMOT0JFVV2jO3TAc/PbVys1RMeuKDl3UPcnR/28o+7HD2Yzh9zx5FC/LT9KNJS4xvtj/y/rUdw6PgJnN0zAa8v3I3HLuvr1mrdmln37CEjChDJMRF4/LJ+uG98b+SWlLtMsJLahbW400FbDegcgwcuOB0vztuJi/p1xKRBnVt+ExEZzmYT/GZUKoalxmFgSmy9TcmHd4/HU7O2YuY9o5BVUIalGcdw84iuKK2w44EvNuCpK/rjme+24YetR/CPXzk2cQ92Trs4u0eC2zGse/xCVCsQHeZIEZbuanqVZl2jeiXAXq1YuScP3907Gte8sbxeyY2Gvpgy0u2YPrh9GCqqqjHl303PtauqVox49ifkOUseHS0sxx11VnR+eucIHCksc7mIoqS8Ck9fOaB20/X1B05u11VWacfjM7egfUQINji3duIqS3CFEpGnVNmrcd2MlfjlaBHmPTAGp8X67sbjXGVJ5J4qezVmbTyMKwd1rp3Uvv5APnp1iG60iXtr/GPeTmTml+KO0T1w7VsrcKLSXu/10GAbfvnrJY3elzptNgDHsF/qtNmY0L8T1uzLQ25JhdvlNhp6+MuN+E+dmmxmmH7DYFzh5vZ47t6/mJARWdj+3BJc8q8l6BofiUcm9sWY3ok+OXzJhIzItxw+fgIv/LADHduH49zeSejdMdrljiFFZZWoqKpGQnQYquzVsInUzsNq6766GdlFuOClxacU/6l69qozXW7x5Iq79y8WhiWysG4JUXjlhsEoPFGJW99bjateX46FO7NdbtFiNSIyQUR2ikiGiExz8fptIpIjIhucjzvNiJPIDKfFRuDl6wfjkYl9Mbp3YpPbt7ULD6mdxxYcZIPNJggOsrU5GQOAXh3aYe9zE9EzyXN1HVurso3FxpvDhIzI4s7v2xGLHhqHZ686EzlF5bj9/TW48vXl+GDZXqw7kI+yBkMTViAiQQBeA3AJgH4AbhCRfi6afqGqg5yPd7waJJGFiQh+fPA8jOuThOSYcDx0cR+sfOR8XNpMrTVPCg32fPrESf1EhNBgG24c0RWTh6bgq3WZePPn3bWVx4NtgjOS22FgSix6JkWjR2IUUhOjkBIXgZBT+Jbr44YDyFDVPQAgIp8DmASgcTl2IjKFiOD924fXO/fajUMQFrQBX68/BAB48+ah+G/6Qfy04+Tm5K/eOBg7sorw6sKMZq8fbBNUVbseLYg0YKcTJmREVCs02IYbhnfF9cO64EhhGTYeLMDGzOPYlHkc3288jMKyk/vXBdsEHduHIzosGJFhQYgKDUZkaBDahYegfUQw2oeHICYiBO3CgxEabINNBDYRBNkcS8bHndHBlxO6zgAO1nmeCWCEi3bXiMgYAL8A+L2qHnTRBiIyBcAUAOja1b15J0TUNs9e7ZjfdbSwHBf374gJAzrhWHE50v76IwDgsoGn4bKBaJSQLfjDeUhNiMKeYyXIL63A4C6x+HbDYZRW2jF5SArOfWFB7VZRNStQPYkJGRE1IiJIjolAckwEJgxw7AagqsgrqcC+3BLsySnBvtwSZBWUobTcjpKKKkxuZ8MAAAwFSURBVJRW2HGsuBxFZVUoPFGJogabDze0+amLfDkhc7WyoeFX5e8AfKaq5SIyFcCHAMa7upiqzgAwA3BM6vdkoERUX3hIENJS4+udS4wOw09/OA/hIfV7ts7pmYDsonJkZBejR1I0AMd+nTWuGXqysGz6Yxciv6QCHyzfV68ciacwISMit4gIEqLDkBAdhqHd4ltsb69WFJdVobCsEpX2alSrwl7tOF+tishQn779ZAKou3NwCoDDdRuoam6dp28D+LsX4iKiNuqZFF3v+aanLkJ4cBAUiiq7e9+T4qJC8fsLWy5m2xaG3hFFZAKAfwEIAvCOqj7f4PXbALwI4JDz1KucGEsUGIJsgpjIEMREtr32kYnWAOgtIt3huD9dD+DGug1EJFlVs5xPrwCw3bshEtGpqLu3pwEjkK1mWAh1VildCMe3zTUiMktVG06K/UJV7zUqDiKi1lLVKhG5F8A8OL5QvqeqW0XkaQDpqjoLwH0icgWAKgB5AG4zLWAi8ntG5oRcpUREfktV5wCY0+DcE3WOHwHwiLfjIqLAZOSMWlerlFxtmHeNiGwSkS9FpIuL1yEiU0QkXUTSc3JyjIiViIiIyDRGJmTurlJKVdWBAH6EY5VS4zepzlDVNFVNS0pK8nCYREREROYyMiFza5WSqpY7n74NYKiB8RARERH5JCMTstpVSiISCscqpVl1G4hI3T0OuEqJiIiILMmwSf1cpURERETkHkMrb3CVEhEREVHLfHbfEiIiIiKrYEJGREREZDJR9a99bkUkB8D+VrwlEcAxg8IxGmM3B2M3R3Oxd1PVgKh508p7mD/+//THmAH/jJsxe8epxuzW/cvvErLWEpF0VU0zO462YOzmYOzm8OfYjeKPvxN/jBnwz7gZs3d4K2YOWRIRERGZjAkZERERkcmskJDNMDuAU8DYzcHYzeHPsRvFH38n/hgz4J9xM2bv8ErMAT+HjIiIiMjXWaGHjIiIiMinMSEjIiIiMlnAJmQiMkFEdopIhohMMzuelojIeyKSLSJb6pyLF5H5IrLL+d84M2N0RUS6iMhCEdkuIltF5H7neX+IPVxEVovIRmfsf3Ge7y4iq5yxfyEioWbH2hQRCRKR9SLyvfO5P8W+T0Q2i8gGEUl3nvP5Pzfe4sv3sNb8vxOH6c6fY5OIDPFSjG7fU5uLUURudbbfJSK3mhDzUyJyyPm73iAiE+u89ogz5p0icnGd8177s9PafwN84XfdTMzm/q5VNeAecGxmvhtADwChADYC6Gd2XC3EPAbAEABb6px7AcA05/E0AH83O04XcScDGOI8bgfgFwD9/CR2ARDtPA4BsArASAD/AXC98/ybAO4yO9ZmfoYHAXwK4Hvnc3+KfR+AxAbnfP7PjZd+Nz59D2vN/zsAEwHMdf59GwlglZdidPue2lSMAOIB7HH+N855HOflmJ8C8EcXbfs5/1yEAeju/PMS5O0/O639N8AXftfNxGzq7zpQe8iGA8hQ1T2qWgHgcwCTTI6pWaq6GEBeg9OTAHzoPP4QwJVeDcoNqpqlquucx0UAtgPoDP+IXVW12Pk0xPlQAOMBfOk875OxA4CIpAC4FMA7zucCP4m9GT7/58ZL/O4ehqb/300C8JHz79tKALEikmx0MK28pzYV48UA5qtqnqrmA5gPYIKXY27KJOD/t3e3MXJVdRzHvz+2UBpaW0AgxCJbtTwEolWKUEFsDGlEGwLy0EoJBkiURq2QQCOS+EpjjU/4wpBQJRAopQpWSiSlSSkaodCmuF1aFK0piU1riyggoA1d/r44Z/Rmndnd2W7n3Fl+n+Rm7py5c/Z/z9w999xzHw4PRMT+iNgJ7CBtNx3ddkaxDyhe1kPE3EpHynq8NsjeA/yl8n4XQxd2XZ0QEXsgbUDA8YXjGZKkXuDDpJ6mrog9n/LrA/aRKoA/A69ExIG8SJ23nduBpcDb+f2xdE/skBq/6yRtkfSFnNYV200H1L0Oa+e3q9O6tBtjXWL/cj69d1flNH7tYh7hPqBWcQ+KGQqW9XhtkKlJmp/vcQhJmgw8BNwYEa+VjmekImIgImYB00lHO6c3W6yzUQ1P0nxgX0RsqSY3WbR2sVecFxEfAS4CviTpgtIB1Ujdf8t2fru6rwu0jrEOsd8BvB+YBewBvp/TaxVzG/uA2sTdJOaiZT1eG2S7gJMq76cDuwvFcjD2Nrr28+u+wvE0Jelw0ka9IiJ+kZO7IvaGiHgFeIJ0TcM0SRPyR3Xdds4DLpb0Iqmb/JOkHrNuiB2AiNidX/cBq0kN4q7abg6hWtdhbf52dVqXdmMsHntE7M0Hjm8Dy0llzRCxdTzmNvcBtYi7Wcyly3q8Nsg2AzOV7jg7AlgIrCkc02isARp3mnweeLhgLE3l65Z+Cvw+In5Q+agbYj9O0rQ8Pwm4kHQtwQbg8rxYLWOPiFsjYnpE9JK278cjYhFdEDuApKMkTWnMA/OAbXTBdtMhta3DRvHbrQGuyXfXnQu82jiVVUC7MT4GzJN0dD59NS+ndcyg6+0uJZV1I+aFkiZKmgHMBDbR4W1nFPuA4mXdKubiZX0wdyrUeSLdyfFH0jVBt5WOZwTxriR1kb5FanVfT7omaD3wp/x6TOk4m8R9PqmLth/oy9OnuyT2DwK/y7FvA76R09+X/9l2AD8HJpaOdZj1mMv/7rLsithznFvztL3xP9oN200Hy6iWdVi7vx3ptM6P83o8B8zuUJwjrlOHihG4Lv8/7QCuLRDzvTmmftLO/sTK8rflmF8ALiqx7bS7D6hDWQ8Rc9Gy9tBJZmZmZoWN11OWZmZmZl3DDTIzMzOzwtwgMzMzMyvMDTIzMzOzwtwgMzMzMyvMDTLrCEkDkvoq09fGMO9eSduGX9LMbGiSXs+vvZKuGuO8vz7o/VNjmb91Nz/2wjpC0usRMfkQ5d1Leg7XmYcifzN752jUVZLmAjdHxPw2vtsTEQPD5T0Wcdr44x4yK0rSi5K+I2lTnj6Q00+WtD4P8rpe0ntz+gmSVkvamqeP5ax6JC2XtF3SuvzkfSQtkfR8zueBQqtpZt1nGfDx3KN/k6QeSd+VtDnXJ18EkDRX0gZJ95MeKoqkXyoNvL5defB1ScuASTm/FTmt0RunnPc2Sc9JWlDJ+wlJD0r6g6QV+SnzSFpWqdu+1/HSsTHnHjLrCEkD5Moq+3ZErFIai3F5RHxL0jXAlRExX9IjwIMRcY+k64CLI+ISSauAjRFxu6QeYDJwNOnJzrMjok/Sz4A1EXGfpN3AjIjYL2lapDErzcyaatVDlhtWx0fENyVNBJ4ErgBOBn4FnBkRO/Oyx0TE3/OB4WbgExHx8uAessrfugy4AfgU8O78nXOAU0lDDp1BGiPxSeAW4HlgI3BaRITrtvHBPWTWKf+KiFmVaVXls5WV1zl5fg5wf56/lzTUBaRBtO8AiDQI7Ks5fWdE9OX5LUBvnu8HVki6GjgwlitkZu8o80hjMPYBz5CGBpqZP9vUaIxlSyRtBZ4mDT49k6GdD6zMddpe4NfA2ZW8d0Ua8LqPVLe9Bvwb+ImkzwJvHvTaWXFukFkdRIv5Vss0s78yPwBMyPOfIY2bdhawRdKEwV80MxsBAV+pHFTOiIh1+bM3/rtQ6lm7EJgTER8ijZd75AjybuX/6raIOAB8FHgIuARY29aaWC25QWZ1sKDyujHPPwUszPOLgN/m+fXAYkgX0Ep6V6tMJR0GnBQRG4ClwDTSKU4zs+H8E5hSef8YsFjS4QCSTpF0VJPvTQX+ERFvSjoNOLfy2VuN7w/yG2BBrtOOAy4ANrUKTNJkYGpEPArcCMxqZ8WsntxbYJ0yKXf1N6yNiMajLyZKeoZ0gPC5nLYEuEvSLcBLwLU5/avAnZKuJx0tLgb2tPibPcB9kqaSjkB/6OsszGyE+oED+dTj3cCPSKcLn80X1r9E6p0abC1wg6R+4AXSacuGO4F+Sc9GxKJK+mrSZRpbSWcDlkbEX3ODrpkpwMOSjiTVbTeNbhWtTnxRvxWVL+qfHRF/Kx2LmZlZKT5laWZmZlaYe8jMzMzMCnMPmZmZmVlhbpCZmZmZFeYGmZmZmVlhbpCZmZmZFeYGmZmZmVlh/wFiyMXE5hExWAAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"## 7. Make predictions"},{"metadata":{"trusted":true},"cell_type":"code","source":"def decode_predictions(text_batch_logits):\n\n    text_batch_tokens = F.softmax(text_batch_logits, 2).argmax(2) # [T, batch_size]\n    text_batch_tokens = text_batch_tokens.numpy().T # [batch_size, T]\n\n    text_batch_tokens_new = []\n    for text_tokens in text_batch_tokens:\n        text = [idx2char[idx] for idx in text_tokens]\n        text = \"\".join(text)\n        text_batch_tokens_new.append(text)\n\n    return text_batch_tokens_new","execution_count":31,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"results_train = pd.DataFrame(columns=['actual', 'prediction'])\ntrain_loader = DataLoader(trainset, batch_size=16, num_workers=1, shuffle=False)\nwith torch.no_grad():\n    for image_batch, text_batch in tqdm(train_loader, leave=True):\n        text_batch_logits = crnn(image_batch.to(device)) # [T, batch_size, num_classes==num_features]\n        text_batch_pred = decode_predictions(text_batch_logits.cpu())\n        #print(text_batch, text_batch_pred)\n        df = pd.DataFrame(columns=['actual', 'prediction'])\n        df['actual'] = text_batch\n        df['prediction'] = text_batch_pred\n        results_train = pd.concat([results_train, df])\nresults_train = results_train.reset_index(drop=True)","execution_count":32,"outputs":[{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=51.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2d3584a3b1ab4061bc72a0c11fca6bee"}},"metadata":{}},{"output_type":"stream","text":"\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"results_test = pd.DataFrame(columns=['actual', 'prediction'])\ntest_loader = DataLoader(testset, batch_size=16, num_workers=1, shuffle=False)\nwith torch.no_grad():\n    for image_batch, text_batch in tqdm(test_loader, leave=True):\n        text_batch_logits = crnn(image_batch.to(device)) # [T, batch_size, num_classes==num_features]\n        text_batch_pred = decode_predictions(text_batch_logits.cpu())\n        #print(text_batch, text_batch_pred)\n        df = pd.DataFrame(columns=['actual', 'prediction'])\n        df['actual'] = text_batch\n        df['prediction'] = text_batch_pred\n        results_test = pd.concat([results_test, df])\nresults_test = results_test.reset_index(drop=True)","execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=17.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"43caa22ac31b45858536eda827c13ed4"}},"metadata":{}},{"output_type":"stream","text":"\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(results_train.shape)\nresults_train.head()","execution_count":34,"outputs":[{"output_type":"stream","text":"(802, 2)\n","name":"stdout"},{"output_type":"execute_result","execution_count":34,"data":{"text/plain":"  actual  prediction\n0  pcm7f  ppccmm77ff\n1  377xx  337777x-xx\n2  6g45w  66gg4455ww\n3  mc35n  mmcc3355nn\n4  5mfff  55mmfff-ff","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>actual</th>\n      <th>prediction</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>pcm7f</td>\n      <td>ppccmm77ff</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>377xx</td>\n      <td>337777x-xx</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>6g45w</td>\n      <td>66gg4455ww</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>mc35n</td>\n      <td>mmcc3355nn</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5mfff</td>\n      <td>55mmfff-ff</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(results_test.shape)\nresults_test.head()","execution_count":35,"outputs":[{"output_type":"stream","text":"(268, 2)\n","name":"stdout"},{"output_type":"execute_result","execution_count":35,"data":{"text/plain":"  actual  prediction\n0  2fxgd  22ffxxggdd\n1  y5n6d  yy55nn66dd\n2  8gmnx  88ggmmnnxx\n3  wm47f  wwmmmm77ff\n4  dn26n  ddnn2266nn","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>actual</th>\n      <th>prediction</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2fxgd</td>\n      <td>22ffxxggdd</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>y5n6d</td>\n      <td>yy55nn66dd</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8gmnx</td>\n      <td>88ggmmnnxx</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>wm47f</td>\n      <td>wwmmmm77ff</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>dn26n</td>\n      <td>ddnn2266nn</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def remove_duplicates(text):\n    if len(text) > 1:\n        letters = [text[0]] + [letter for idx, letter in enumerate(text[1:], start=1) if text[idx] != text[idx-1]]\n    elif len(text) == 1:\n        letters = [text[0]]\n    else:\n        return \"\"\n    return \"\".join(letters)\n\ndef correct_prediction(word):\n    parts = word.split(\"-\")\n    parts = [remove_duplicates(part) for part in parts]\n    corrected_word = \"\".join(parts)\n    return corrected_word","execution_count":36,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"results_train['prediction_corrected'] = results_train['prediction'].apply(correct_prediction)\nresults_train.head()","execution_count":37,"outputs":[{"output_type":"execute_result","execution_count":37,"data":{"text/plain":"  actual  prediction prediction_corrected\n0  pcm7f  ppccmm77ff                pcm7f\n1  377xx  337777x-xx                 37xx\n2  6g45w  66gg4455ww                6g45w\n3  mc35n  mmcc3355nn                mc35n\n4  5mfff  55mmfff-ff                 5mff","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>actual</th>\n      <th>prediction</th>\n      <th>prediction_corrected</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>pcm7f</td>\n      <td>ppccmm77ff</td>\n      <td>pcm7f</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>377xx</td>\n      <td>337777x-xx</td>\n      <td>37xx</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>6g45w</td>\n      <td>66gg4455ww</td>\n      <td>6g45w</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>mc35n</td>\n      <td>mmcc3355nn</td>\n      <td>mc35n</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5mfff</td>\n      <td>55mmfff-ff</td>\n      <td>5mff</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"results_test['prediction_corrected'] = results_test['prediction'].apply(correct_prediction)\nresults_test.head()","execution_count":38,"outputs":[{"output_type":"execute_result","execution_count":38,"data":{"text/plain":"  actual  prediction prediction_corrected\n0  2fxgd  22ffxxggdd                2fxgd\n1  y5n6d  yy55nn66dd                y5n6d\n2  8gmnx  88ggmmnnxx                8gmnx\n3  wm47f  wwmmmm77ff                 wm7f\n4  dn26n  ddnn2266nn                dn26n","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>actual</th>\n      <th>prediction</th>\n      <th>prediction_corrected</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2fxgd</td>\n      <td>22ffxxggdd</td>\n      <td>2fxgd</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>y5n6d</td>\n      <td>yy55nn66dd</td>\n      <td>y5n6d</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8gmnx</td>\n      <td>88ggmmnnxx</td>\n      <td>8gmnx</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>wm47f</td>\n      <td>wwmmmm77ff</td>\n      <td>wm7f</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>dn26n</td>\n      <td>ddnn2266nn</td>\n      <td>dn26n</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## 8. Evaluate the model"},{"metadata":{"trusted":true},"cell_type":"code","source":"mistakes_df = results_test[results_test['actual'] != results_test['prediction_corrected']]\nmistakes_df","execution_count":39,"outputs":[{"output_type":"execute_result","execution_count":39,"data":{"text/plain":"    actual  prediction prediction_corrected\n3    wm47f  wwmmmm77ff                 wm7f\n42   pmg55  ppmmgg5555                 pmg5\n64   mddgb  mmddddggbb                 mdgb\n74   mmy5n  mmmmyy55nn                 my5n\n77   6n6gg  66nn66gggg                 6n6g\n78   664nf  666644nnff                 64nf\n80   x775w  xx777755ww                 x75w\n81   55y2m  5555yy22mm                 5y2m\n88   f2fge  ff22ggggee                 f2ge\n92   d666m  dd6---66mm                 d66m\n94   5p3mm  55pp33mmmm                 5p3m\n107  f22bn  ff2222bbnn                 f2bn\n115  7nnnx  77n---nnxx                 7nnx\n116  b5nmm  bb55nnmmmm                 b5nm\n121  y32yy  yy3322yyyy                 y32y\n137  8cccc  88c------c                  8cc\n149  ncyx8  nncccxxx88                 ncx8\n165  ddcdd  d-ddccdddd                 ddcd\n176  ddxpp  d-ddxxpppp                 ddxp\n178  6dd2y  66dddd22yy                 6d2y\n180  78eec  7788eeeecc                 78ec\n183  gxxpf  ggxxxxppff                 gxpf\n190  n6nn2  nn66nnnn22                 n6n2\n196  mxnw4  mmxxmmww44                mxmw4\n217  nnn57  n---nn5577                 nn57\n227  nnfx3  nnnnffxx33                 nfx3\n229  5nggg  55nng----g                 5ngg\n233  x4gg5  xx44gggg55                 x4g5\n254  wgnwp  wwggmmwwpp                wgmwp\n255  25w53  2255555533                  253\n256  22d5n  2222dd55nn                 2d5n\n260  mm3nn  mmmm33n-nn                 m3nn\n267  n4xx5  nn44xxxx55                 n4x5","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>actual</th>\n      <th>prediction</th>\n      <th>prediction_corrected</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td>wm47f</td>\n      <td>wwmmmm77ff</td>\n      <td>wm7f</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>pmg55</td>\n      <td>ppmmgg5555</td>\n      <td>pmg5</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>mddgb</td>\n      <td>mmddddggbb</td>\n      <td>mdgb</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>mmy5n</td>\n      <td>mmmmyy55nn</td>\n      <td>my5n</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>6n6gg</td>\n      <td>66nn66gggg</td>\n      <td>6n6g</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>664nf</td>\n      <td>666644nnff</td>\n      <td>64nf</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>x775w</td>\n      <td>xx777755ww</td>\n      <td>x75w</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>55y2m</td>\n      <td>5555yy22mm</td>\n      <td>5y2m</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>f2fge</td>\n      <td>ff22ggggee</td>\n      <td>f2ge</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>d666m</td>\n      <td>dd6---66mm</td>\n      <td>d66m</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>5p3mm</td>\n      <td>55pp33mmmm</td>\n      <td>5p3m</td>\n    </tr>\n    <tr>\n      <th>107</th>\n      <td>f22bn</td>\n      <td>ff2222bbnn</td>\n      <td>f2bn</td>\n    </tr>\n    <tr>\n      <th>115</th>\n      <td>7nnnx</td>\n      <td>77n---nnxx</td>\n      <td>7nnx</td>\n    </tr>\n    <tr>\n      <th>116</th>\n      <td>b5nmm</td>\n      <td>bb55nnmmmm</td>\n      <td>b5nm</td>\n    </tr>\n    <tr>\n      <th>121</th>\n      <td>y32yy</td>\n      <td>yy3322yyyy</td>\n      <td>y32y</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td>8cccc</td>\n      <td>88c------c</td>\n      <td>8cc</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>ncyx8</td>\n      <td>nncccxxx88</td>\n      <td>ncx8</td>\n    </tr>\n    <tr>\n      <th>165</th>\n      <td>ddcdd</td>\n      <td>d-ddccdddd</td>\n      <td>ddcd</td>\n    </tr>\n    <tr>\n      <th>176</th>\n      <td>ddxpp</td>\n      <td>d-ddxxpppp</td>\n      <td>ddxp</td>\n    </tr>\n    <tr>\n      <th>178</th>\n      <td>6dd2y</td>\n      <td>66dddd22yy</td>\n      <td>6d2y</td>\n    </tr>\n    <tr>\n      <th>180</th>\n      <td>78eec</td>\n      <td>7788eeeecc</td>\n      <td>78ec</td>\n    </tr>\n    <tr>\n      <th>183</th>\n      <td>gxxpf</td>\n      <td>ggxxxxppff</td>\n      <td>gxpf</td>\n    </tr>\n    <tr>\n      <th>190</th>\n      <td>n6nn2</td>\n      <td>nn66nnnn22</td>\n      <td>n6n2</td>\n    </tr>\n    <tr>\n      <th>196</th>\n      <td>mxnw4</td>\n      <td>mmxxmmww44</td>\n      <td>mxmw4</td>\n    </tr>\n    <tr>\n      <th>217</th>\n      <td>nnn57</td>\n      <td>n---nn5577</td>\n      <td>nn57</td>\n    </tr>\n    <tr>\n      <th>227</th>\n      <td>nnfx3</td>\n      <td>nnnnffxx33</td>\n      <td>nfx3</td>\n    </tr>\n    <tr>\n      <th>229</th>\n      <td>5nggg</td>\n      <td>55nng----g</td>\n      <td>5ngg</td>\n    </tr>\n    <tr>\n      <th>233</th>\n      <td>x4gg5</td>\n      <td>xx44gggg55</td>\n      <td>x4g5</td>\n    </tr>\n    <tr>\n      <th>254</th>\n      <td>wgnwp</td>\n      <td>wwggmmwwpp</td>\n      <td>wgmwp</td>\n    </tr>\n    <tr>\n      <th>255</th>\n      <td>25w53</td>\n      <td>2255555533</td>\n      <td>253</td>\n    </tr>\n    <tr>\n      <th>256</th>\n      <td>22d5n</td>\n      <td>2222dd55nn</td>\n      <td>2d5n</td>\n    </tr>\n    <tr>\n      <th>260</th>\n      <td>mm3nn</td>\n      <td>mmmm33n-nn</td>\n      <td>m3nn</td>\n    </tr>\n    <tr>\n      <th>267</th>\n      <td>n4xx5</td>\n      <td>nn44xxxx55</td>\n      <td>n4x5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(mistakes_df['prediction_corrected'].str.len().value_counts())","execution_count":40,"outputs":[{"output_type":"stream","text":"4    29\n5     2\n3     2\nName: prediction_corrected, dtype: int64\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"mask = mistakes_df['prediction_corrected'].str.len() == 5\nmistakes_df[mask]","execution_count":41,"outputs":[{"output_type":"execute_result","execution_count":41,"data":{"text/plain":"    actual  prediction prediction_corrected\n196  mxnw4  mmxxmmww44                mxmw4\n254  wgnwp  wwggmmwwpp                wgmwp","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>actual</th>\n      <th>prediction</th>\n      <th>prediction_corrected</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>196</th>\n      <td>mxnw4</td>\n      <td>mmxxmmww44</td>\n      <td>mxmw4</td>\n    </tr>\n    <tr>\n      <th>254</th>\n      <td>wgnwp</td>\n      <td>wwggmmwwpp</td>\n      <td>wgmwp</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"mistake_image_fp = os.path.join(data_path, mistakes_df[mask]['actual'].values[0] + \".png\")\nprint(mistake_image_fp)\nmistake_image = Image.open(mistake_image_fp)\nplt.imshow(mistake_image)\nplt.show()","execution_count":42,"outputs":[{"output_type":"stream","text":"/kaggle/input/captcha-version-2-images/samples/mxnw4.png\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_accuracy = accuracy_score(results_train['actual'], results_train['prediction_corrected'])\nprint(train_accuracy)\ntest_accuracy = accuracy_score(results_test['actual'], results_test['prediction_corrected'])\nprint(test_accuracy)","execution_count":43,"outputs":[{"output_type":"stream","text":"0.92643391521197\n0.8768656716417911\n","name":"stdout"}]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}