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\n" @@ -3243,7 +3243,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "8.3 ms ± 1.43 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "8.6 ms ± 1.36 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -3261,7 +3261,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "57 µs ± 21.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "54.1 µs ± 24.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -3279,7 +3279,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "47.6 µs ± 28.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "The slowest run took 6.82 times longer than the fastest. This could mean that an intermediate result is being cached.\n", + "42.1 µs ± 29.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] }, { @@ -3601,8 +3602,39 @@ "bs = 256\n", "lr = 0.4\n", "train_dl, valid_dl = get_dls(train_ds, valid_ds, bs, collate_fn=collate_device)\n", - "opt = optim.SGD(simple_cnn.parameters(), lr=lr)\n", - " \n" + "opt = optim.SGD(simple_cnn.parameters(), lr=lr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f75175bc", + "metadata": {}, + "outputs": [], + "source": [ + "def fit(epochs, model, loss_func, opt, train_dl, valid_dl):\n", + " for epoch in range(epochs):\n", + " model.train()\n", + " for xb, yb in train_dl:\n", + " preds = model(xb)\n", + " loss = loss_func(preds, yb)\n", + " loss.backward()\n", + " opt.step()\n", + " opt.zero_grad()\n", + " \n", + " model.eval()\n", + " with torch.no_grad():\n", + " total_loss, total_acc, count = 0., 0., 0\n", + " for xb, yb in valid_dl:\n", + " preds = model(xb)\n", + " n = len(xb)\n", + " count += n\n", + " total_loss += loss_func(preds, yb).item()*n\n", + " total_acc += accuracy(preds, yb).item()*n\n", + " \n", + " print(epoch, total_loss/count, total_acc/count)\n", + " \n", + " return total_loss/count, total_acc/count " ] }, { @@ -3610,19 +3642,7 @@ "execution_count": null, "id": "8338744f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 0.2615232038497925 0.0\n", - "1 0.1606189539670944 0.0\n", - "2 0.12759257712364197 0.0\n", - "3 0.23388885581493377 0.0\n", - "4 0.09892643284797668 0.0\n" - ] - } - ], + "outputs": [], "source": [ "loss,acc = fit(5, simple_cnn.to(def_device), F.cross_entropy, opt, train_dl, valid_dl)" ] diff --git a/07_convolutions.ipynb b/07_convolutions.ipynb index 5779c892..7c54e466 100644 --- a/07_convolutions.ipynb +++ b/07_convolutions.ipynb @@ -118,343 +118,343 @@ "data": { "text/html": [ "\n", - "\n", + "
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70.00.00.00.00.00.00.0-0.7-1.6-2.4-2.6-2.7-2.9-3.0-3.0-2.9-2.0-1.0-0.1-0.3-0.5-0.5-0.20.00.00.070.00.00.00.00.00.00.0-0.7-1.6-2.4-2.6-2.7-2.9-3.0-3.0-2.9-2.0-1.0-0.1-0.3-0.5-0.5-0.20.00.00.0
80.00.00.00.00.00.00.0-0.4-1.4-2.4-2.9-2.4-1.9-1.6-1.6-1.4-1.0-0.4-0.0-0.4-0.7-0.7-0.20.00.00.080.00.00.00.00.00.00.0-0.4-1.4-2.4-2.9-2.4-1.9-1.6-1.6-1.4-1.0-0.4-0.0-0.4-0.7-0.7-0.20.00.00.0
90.00.00.00.00.00.00.0-0.0-0.1-0.2-0.3-0.2-0.10.00.31.01.10.80.1-0.6-0.6-0.6-0.00.00.00.090.00.00.00.00.00.00.0-0.0-0.1-0.2-0.3-0.2-0.10.00.31.01.10.80.1-0.6-0.6-0.6-0.00.00.00.0
100.00.00.00.00.00.00.00.00.00.00.00.00.41.22.12.41.70.7-0.2-0.7-0.7-0.50.00.00.00.0100.00.00.00.00.00.00.00.00.00.00.00.00.41.22.12.41.70.7-0.2-0.7-0.7-0.50.00.00.00.0
110.00.00.00.00.00.00.00.10.61.42.02.32.52.72.61.90.8-0.4-1.5-1.5-0.9-0.10.00.00.00.0110.00.00.00.00.00.00.00.10.61.42.02.32.52.72.61.90.8-0.4-1.5-1.5-0.9-0.10.00.00.00.0
120.00.00.00.00.00.00.21.02.02.83.03.02.61.80.80.2-0.3-1.3-2.1-1.8-0.8-0.00.00.00.00.0120.00.00.00.00.00.00.21.02.02.83.03.02.61.80.80.2-0.3-1.3-2.1-1.8-0.8-0.00.00.00.00.0
130.00.00.00.00.00.00.20.91.41.51.00.70.50.20.00.00.1-0.0-0.2-0.2-0.10.00.00.00.00.0130.00.00.00.00.00.00.20.91.41.51.00.70.50.20.00.00.1-0.0-0.2-0.2-0.10.00.00.00.00.0
140.00.00.00.00.00.0-0.2-0.9-1.4-2.0-2.1-2.4-2.4-2.4-2.1-1.2-0.10.60.60.30.00.00.00.00.00.0140.00.00.00.00.00.0-0.2-0.9-1.4-2.0-2.1-2.4-2.4-2.4-2.1-1.2-0.10.60.60.30.00.00.00.00.00.0
150.00.00.00.00.00.0-0.2-1.0-2.0-2.8-3.0-3.0-3.0-3.0-2.9-1.9-0.9-0.0-0.00.00.00.00.00.00.00.0150.00.00.00.00.00.0-0.2-1.0-2.0-2.8-3.0-3.0-3.0-3.0-2.9-1.9-0.9-0.0-0.00.00.00.00.00.00.00.0
160.00.00.00.00.00.00.0-0.1-0.6-0.8-0.9-0.5-0.5-0.5-0.6-0.4-0.20.00.00.00.00.00.00.00.00.0160.00.00.00.00.00.00.0-0.1-0.6-0.8-0.9-0.5-0.5-0.5-0.6-0.4-0.20.00.00.00.00.00.00.00.00.0
170.00.00.00.00.00.30.30.30.00.00.00.00.00.41.31.30.90.00.00.00.00.00.00.00.00.0170.00.00.00.00.00.30.30.30.00.00.00.00.00.41.31.30.90.00.00.00.00.00.00.00.00.0
180.00.00.00.21.01.91.81.10.10.00.00.10.41.41.91.60.6-0.1-0.1-0.00.00.00.00.00.00.0180.00.00.00.21.01.91.81.10.10.00.00.10.41.41.91.60.6-0.1-0.1-0.00.00.00.00.00.00.0
190.00.00.00.81.82.52.21.81.61.61.61.82.32.41.60.4-0.9-1.2-0.9-0.30.00.00.00.00.00.0190.00.00.00.81.82.52.21.81.61.61.61.82.32.41.60.4-0.9-1.2-0.9-0.30.00.00.00.00.00.0
200.00.00.00.60.80.81.11.92.83.03.02.92.51.50.2-1.4-2.3-2.2-1.2-0.30.00.00.00.00.00.0200.00.00.00.60.80.81.11.92.83.03.02.92.51.50.2-1.4-2.3-2.2-1.2-0.30.00.00.00.00.00.0
210.00.00.0-0.6-1.0-1.00.00.91.31.41.20.8-0.1-1.2-2.2-2.5-2.0-1.1-0.30.00.00.00.00.00.00.0210.00.00.0-0.6-1.0-1.00.00.91.31.41.20.8-0.1-1.2-2.2-2.5-2.0-1.1-0.30.00.00.00.00.00.00.0
220.00.00.0-0.8-1.8-2.4-1.6-0.6-0.4-0.9-1.8-2.5-2.9-2.9-2.5-1.5-0.6-0.00.00.00.00.00.00.00.00.0220.00.00.0-0.8-1.8-2.4-1.6-0.6-0.4-0.9-1.8-2.5-2.9-2.9-2.5-1.5-0.6-0.00.00.00.00.00.00.00.00.0
230.00.00.0-0.2-0.8-1.8-2.6-3.0-3.0-3.0-2.8-2.6-2.2-1.6-0.7-0.20.00.00.00.00.00.00.00.00.00.0230.00.00.0-0.2-0.8-1.8-2.6-3.0-3.0-3.0-2.8-2.6-2.2-1.6-0.7-0.20.00.00.00.00.00.00.00.00.00.0
240.00.00.00.0-0.0-0.4-1.3-2.3-2.6-2.1-1.2-0.5-0.10.00.00.00.00.00.00.00.00.00.00.00.00.0240.00.00.00.0-0.0-0.4-1.3-2.3-2.6-2.1-1.2-0.5-0.10.00.00.00.00.00.00.00.00.00.00.00.00.0
250.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0250.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
\n" @@ -3243,7 +3243,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "8.3 ms ± 1.43 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "8.6 ms ± 1.36 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -3261,7 +3261,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "57 µs ± 21.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "54.1 µs ± 24.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -3279,7 +3279,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "47.6 µs ± 28.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "The slowest run took 6.82 times longer than the fastest. This could mean that an intermediate result is being cached.\n", + "42.1 µs ± 29.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] }, { @@ -3604,24 +3605,44 @@ "opt = optim.SGD(simple_cnn.parameters(), lr=lr)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "f75175bc", + "metadata": {}, + "outputs": [], + "source": [ + "def fit(epochs, model, loss_func, opt, train_dl, valid_dl):\n", + " for epoch in range(epochs):\n", + " model.train()\n", + " for xb, yb in train_dl:\n", + " preds = model(xb)\n", + " loss = loss_func(preds, yb)\n", + " loss.backward()\n", + " opt.step()\n", + " opt.zero_grad()\n", + " \n", + " model.eval()\n", + " with torch.no_grad():\n", + " total_loss, total_acc, count = 0., 0., 0\n", + " for xb, yb in valid_dl:\n", + " preds = model(xb)\n", + " n = len(xb)\n", + " count += n\n", + " total_loss += loss_func(preds, yb).item()*n\n", + " total_acc += accuracy(preds, yb).item()*n\n", + " \n", + " print(epoch, total_loss/count, total_acc/count)\n", + " \n", + " return total_loss/count, total_acc/count " + ] + }, { "cell_type": "code", "execution_count": null, "id": "8338744f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 0.2615232038497925 0.0\n", - "1 0.1606189539670944 0.0\n", - "2 0.12759257712364197 0.0\n", - "3 0.23388885581493377 0.0\n", - "4 0.09892643284797668 0.0\n" - ] - } - ], + "outputs": [], "source": [ "loss,acc = fit(5, simple_cnn.to(def_device), F.cross_entropy, opt, train_dl, valid_dl)" ] diff --git a/nbs/04_mini_batch_training.ipynb b/nbs/04_mini_batch_training.ipynb index fade6c19..4f9de714 100644 --- a/nbs/04_mini_batch_training.ipynb +++ b/nbs/04_mini_batch_training.ipynb @@ -166,13 +166,13 @@ { "data": { "text/plain": [ - "tensor([[-2.3917, -2.3172, -2.1445, ..., -2.3604, -2.4435, -2.3298],\n", - " [-2.3426, -2.2119, -2.2799, ..., -2.3664, -2.4151, -2.2220],\n", - " [-2.3725, -2.2966, -2.2658, ..., -2.2858, -2.3270, -2.3698],\n", + "tensor([[-2.3105, -2.3146, -2.1153, ..., -2.2368, -2.4307, -2.4524],\n", + " [-2.3483, -2.3038, -2.2801, ..., -2.2111, -2.5152, -2.3177],\n", + " [-2.4311, -2.3641, -2.1093, ..., -2.1862, -2.4990, -2.3351],\n", " ...,\n", - " [-2.4004, -2.3082, -2.1309, ..., -2.3633, -2.4319, -2.2571],\n", - " [-2.4322, -2.3229, -2.1224, ..., -2.3613, -2.4487, -2.2554],\n", - " [-2.3660, -2.2850, -2.0563, ..., -2.3602, -2.5124, -2.3140]],\n", + " [-2.3080, -2.3101, -2.1713, ..., -2.2585, -2.3865, -2.3988],\n", + " [-2.3447, -2.2730, -2.1000, ..., -2.3432, -2.4177, -2.3100],\n", + " [-2.3322, -2.2556, -2.1236, ..., -2.3390, -2.3710, -2.4355]],\n", " grad_fn=)" ] }, @@ -216,13 +216,13 @@ { "data": { "text/plain": [ - "tensor([[-2.3917, -2.3172, -2.1445, ..., -2.3604, -2.4435, -2.3298],\n", - " [-2.3426, -2.2119, -2.2799, ..., -2.3664, -2.4151, -2.2220],\n", - " [-2.3725, -2.2966, -2.2658, ..., -2.2858, -2.3270, -2.3698],\n", + "tensor([[-2.3105, -2.3146, -2.1153, ..., -2.2368, -2.4307, -2.4524],\n", + " [-2.3483, -2.3038, -2.2801, ..., -2.2111, -2.5152, -2.3177],\n", + " [-2.4311, -2.3641, -2.1093, ..., -2.1862, -2.4990, -2.3351],\n", " ...,\n", - " [-2.4004, -2.3082, -2.1309, ..., -2.3633, -2.4319, -2.2571],\n", - " [-2.4322, -2.3229, -2.1224, ..., -2.3613, -2.4487, -2.2554],\n", - " [-2.3660, -2.2850, -2.0563, ..., -2.3602, -2.5124, -2.3140]],\n", + " [-2.3080, -2.3101, -2.1713, ..., -2.2585, -2.3865, -2.3988],\n", + " [-2.3447, -2.2730, -2.1000, ..., -2.3432, -2.4177, -2.3100],\n", + " [-2.3322, -2.2556, -2.1236, ..., -2.3390, -2.3710, -2.4355]],\n", " grad_fn=)" ] }, @@ -279,13 +279,13 @@ { "data": { "text/plain": [ - "tensor([[-2.3917, -2.3172, -2.1445, ..., -2.3604, -2.4435, -2.3298],\n", - " [-2.3426, -2.2119, -2.2799, ..., -2.3664, -2.4151, -2.2220],\n", - " [-2.3725, -2.2966, -2.2658, ..., -2.2858, -2.3270, -2.3698],\n", + "tensor([[-2.3105, -2.3146, -2.1153, ..., -2.2368, -2.4307, -2.4524],\n", + " [-2.3483, -2.3038, -2.2801, ..., -2.2111, -2.5152, -2.3177],\n", + " [-2.4311, -2.3641, -2.1093, ..., -2.1862, -2.4990, -2.3351],\n", " ...,\n", - " [-2.4004, -2.3082, -2.1309, ..., -2.3633, -2.4319, -2.2571],\n", - " [-2.4322, -2.3229, -2.1224, ..., -2.3613, -2.4487, -2.2554],\n", - " [-2.3660, -2.2850, -2.0563, ..., -2.3602, -2.5124, -2.3140]],\n", + " [-2.3080, -2.3101, -2.1713, ..., -2.2585, -2.3865, -2.3988],\n", + " [-2.3447, -2.2730, -2.1000, ..., -2.3432, -2.4177, -2.3100],\n", + " [-2.3322, -2.2556, -2.1236, ..., -2.3390, -2.3710, -2.4355]],\n", " grad_fn=)" ] }, @@ -329,13 +329,13 @@ { "data": { "text/plain": [ - "tensor([[-2.3917, -2.3172, -2.1445, ..., -2.3604, -2.4435, -2.3298],\n", - " [-2.3426, -2.2119, -2.2799, ..., -2.3664, -2.4151, -2.2220],\n", - " [-2.3725, -2.2966, -2.2658, ..., -2.2858, -2.3270, -2.3698],\n", + "tensor([[-2.3105, -2.3146, -2.1153, ..., -2.2368, -2.4307, -2.4524],\n", + " [-2.3483, -2.3038, -2.2801, ..., -2.2111, -2.5152, -2.3177],\n", + " [-2.4311, -2.3641, -2.1093, ..., -2.1862, -2.4990, -2.3351],\n", " ...,\n", - 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+514,8 @@ { "data": { "text/plain": [ - "tensor([-0.0843, -0.0098, 0.1629, 0.1187, 0.1040, 0.0934, -0.1870, -0.0530,\n", - " -0.1361, -0.0224], grad_fn=)" + "tensor([ 0.0027, -0.0013, 0.1980, 0.0608, -0.1618, 0.0345, 0.0952, 0.0764,\n", + " -0.1174, -0.1391], grad_fn=)" ] }, "execution_count": null, @@ -540,7 +540,7 @@ { "data": { "text/plain": [ - "tensor(2.2846, grad_fn=)" + "tensor(2.3156, grad_fn=)" ] }, "execution_count": null, @@ -561,7 +561,7 @@ { "data": { "text/plain": [ - "tensor(0.1400)" + "tensor(0.0600)" ] }, "execution_count": null, @@ -596,7 +596,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss: 2.30, accuracy: 0.14\n" + "loss: 2.30, accuracy: 0.06\n" ] } ], @@ -614,7 +614,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss: 2.28, accuracy: 0.14\n" + "loss: 2.32, accuracy: 0.06\n" ] } ], @@ -638,9 +638,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss: 0.17, accuracy: 0.94\n", - "loss: 0.13, accuracy: 0.94\n", - "loss: 0.13, accuracy: 0.96\n" + "loss: 0.08, accuracy: 0.98\n", + "loss: 0.07, accuracy: 0.98\n", + "loss: 0.03, accuracy: 1.00\n" ] } ], @@ -731,12 +731,12 @@ "data": { "text/plain": [ "[Parameter containing:\n", - " tensor([[-0.4626, -0.5572, -0.2930],\n", - " [-0.2142, 0.2954, -0.5759],\n", - " [-0.0873, 0.5067, 0.0329],\n", - " [ 0.1627, 0.2251, -0.2415]], requires_grad=True),\n", + " tensor([[-0.3483, -0.5673, 0.0903],\n", + " [ 0.3015, -0.0302, 0.1472],\n", + " [ 0.4863, -0.3693, 0.5638],\n", + " [ 0.2693, -0.4972, 0.1921]], requires_grad=True),\n", " Parameter containing:\n", - " tensor([-0.4074, 0.0654, 0.3297, -0.2555], requires_grad=True)]" + " tensor([-0.1241, -0.4176, -0.2312, -0.1247], requires_grad=True)]" ] }, "execution_count": null, @@ -867,9 +867,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss: 0.02, accuracy: 1.00\n", - "loss: 0.05, accuracy: 0.98\n", - "loss: 0.03, accuracy: 1.00\n" + "loss: 0.13, accuracy: 0.94\n", + "loss: 0.10, accuracy: 0.96\n", + "loss: 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+1390,8 @@ "output_type": "stream", "text": [ "loss: 0.13, accuracy: 0.96\n", - "loss: 0.10, accuracy: 0.98\n", - "loss: 0.12, accuracy: 0.96\n" + "loss: 0.11, accuracy: 0.94\n", + "loss: 0.09, accuracy: 0.96\n" ] } ], @@ -1505,9 +1505,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss: 0.16, accuracy: 0.96\n", - "loss: 0.11, accuracy: 0.98\n", - "loss: 0.07, accuracy: 0.98\n" + "loss: 0.12, accuracy: 0.98\n", + "loss: 0.11, accuracy: 0.96\n", + "loss: 0.11, accuracy: 0.96\n" ] } ], @@ -1722,7 +1722,7 @@ "outputs": [ { "data": { - "image/png": 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