|
131 | 131 | "import scipy.signal\n", |
132 | 132 | "import numpy as np\n", |
133 | 133 | "import matplotlib.pyplot as plt\n", |
| 134 | + "import IPython\n", |
134 | 135 | "\n", |
135 | 136 | "import torch.nn as nn\n", |
136 | 137 | "import torch.nn.functional as F\n", |
137 | 138 | "\n", |
138 | 139 | "import torchvision.transforms as transforms\n", |
139 | 140 | "import torchvision.datasets as datasets\n", |
| 141 | + "from torchvision.utils import make_grid\n", |
140 | 142 | "from torch.utils.data import DataLoader\n", |
141 | 143 | "\n", |
142 | 144 | "from tqdm.notebook import tqdm, trange\n", |
|
4093 | 4095 | "source": [ |
4094 | 4096 | "# @title Finetune ResNet\n", |
4095 | 4097 | "pretrained_accs = []\n", |
4096 | | - "for epoch in tqdm.tqdm(range(10)):\n", |
| 4098 | + "for epoch in tqdm(range(10)):\n", |
4097 | 4099 | " # Train loop\n", |
4098 | 4100 | " for batch in pokemon_train_loader:\n", |
4099 | 4101 | " images, labels = batch\n", |
|
4176 | 4178 | "source": [ |
4177 | 4179 | "# @title Finetune readout of ResNet\n", |
4178 | 4180 | "linreadout_accs = []\n", |
4179 | | - "for epoch in tqdm.tqdm(range(10)):\n", |
| 4181 | + "for epoch in tqdm(range(10)):\n", |
4180 | 4182 | " # Train loop\n", |
4181 | 4183 | " for batch in pokemon_train_loader:\n", |
4182 | 4184 | " images, labels = batch\n", |
|
4258 | 4260 | "source": [ |
4259 | 4261 | "# @title Train ResNet from scratch\n", |
4260 | 4262 | "scratch_accs = []\n", |
4261 | | - "for epoch in tqdm.tqdm(range(10)):\n", |
| 4263 | + "for epoch in tqdm(range(10)):\n", |
4262 | 4264 | " # Train loop\n", |
4263 | 4265 | " for batch in pokemon_train_loader:\n", |
4264 | 4266 | " images, labels = batch\n", |
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