anton/mlpractical
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Removing std out

Browse Source
This commit is contained in:
AntreasAntoniou 2017-10-06 14:49:48 +01:00
parent 5820d0a35c
commit bede9e3dd5
1 changed files with 7 additions and 79 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

86
notebooks/03_Multiple_layer_models.ipynb
View File

@ -176,7 +176,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -217,7 +217,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -268,68 +268,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 5: 4.1s to complete\n",
" error(train)=3.11e-01, acc(train)=9.13e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n",
"Epoch 10: 3.8s to complete\n",
" error(train)=2.89e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.23e-01\n",
"Epoch 15: 5.1s to complete\n",
" error(train)=2.79e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.24e-01\n",
"Epoch 20: 4.0s to complete\n",
" error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
"Epoch 25: 4.1s to complete\n",
" error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
"Epoch 30: 4.8s to complete\n",
" error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n",
"Epoch 35: 6.0s to complete\n",
" error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
"Epoch 40: 4.4s to complete\n",
" error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
"Epoch 45: 4.3s to complete\n",
" error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
"Epoch 50: 6.6s to complete\n",
" error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
"Epoch 55: 4.0s to complete\n",
" error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
"Epoch 60: 3.9s to complete\n",
" error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
"Epoch 65: 3.9s to complete\n",
" error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
"Epoch 70: 5.1s to complete\n",
" error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.31e-01\n",
"Epoch 75: 4.8s to complete\n",
" error(train)=2.47e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
"Epoch 80: 5.1s to complete\n",
" error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
"Epoch 85: 4.2s to complete\n",
" error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
"Epoch 90: 4.8s to complete\n",
" error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
"Epoch 95: 4.4s to complete\n",
" error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
"Epoch 100: 4.2s to complete\n",
" error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n"
]
},
{
"ename": "ValueError",
"evalue": "too many values to unpack (expected 2)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-642f4d4ba05f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m _ = train_model_and_plot_stats(\n\u001b[0;32m---> 35\u001b[0;31m model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n\u001b[0m",
"\u001b[0;32m<ipython-input-2-6cae5fb8b536>\u001b[0m in \u001b[0;36mtrain_model_and_plot_stats\u001b[0;34m(model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m# Run the optimiser for 5 epochs (full passes through the training set)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;31m# printing statistics every epoch.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mstats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimiser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstats_interval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstats_interval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;31m# Plot the change in the validation and training set error over training.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"
]
}
],
"source": [ "source": [
"# Set training run hyperparameters\n", "# Set training run hyperparameters\n",
"batch_size = 100 # number of data points in a batch\n", "batch_size = 100 # number of data points in a batch\n",
@ -594,7 +535,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -647,22 +588,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"ename": "NotImplementedError",
"evalue": "Delete this raise statement and write your code here instead.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-43646f1c28da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m [-4.27819393, 0., 7.11577763]])\n\u001b[1;32m 9\u001b[0m \u001b[0mtanh_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTanhLayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mtanh_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtanh_layer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfprop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mall_correct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtanh_outputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mtest_tanh_outputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-6-4f7b50b779ab>\u001b[0m in \u001b[0;36mfprop\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mFor\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mx\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0my\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mcorresponds\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtanh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \"\"\"\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Delete this raise statement and write your code here instead.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mbprop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrads_wrt_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNotImplementedError\u001b[0m: Delete this raise statement and write your code here instead."
]
}
],
"source": [ "source": [
"test_inputs = np.array([[0.1, -0.2, 0.3], [-0.4, 0.5, -0.6]])\n", "test_inputs = np.array([[0.1, -0.2, 0.3], [-0.4, 0.5, -0.6]])\n",
"test_grads_wrt_outputs = np.array([[5., 10., -10.], [-5., 0., 10.]])\n", "test_grads_wrt_outputs = np.array([[5., 10., -10.], [-5., 0., 10.]])\n",