diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb index bbec2f13..2e1032ec 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb @@ -180,7 +180,7 @@ " loss = cross_entropy(pred, y)\n", " \n", " # Variables to update, i.e. trainable variables.\n", - " trainable_variables = weights.values() + biases.values()\n", + " trainable_variables = list(weights.values()) + list(biases.values())\n", "\n", " # Compute gradients.\n", " gradients = g.gradient(loss, trainable_variables)\n", @@ -198,36 +198,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "step: 100, loss: 567.292969, accuracy: 0.136719\n", - "step: 200, loss: 398.614929, accuracy: 0.562500\n", - "step: 300, loss: 226.743774, accuracy: 0.753906\n", - "step: 400, loss: 193.384521, accuracy: 0.777344\n", - "step: 500, loss: 138.649963, accuracy: 0.886719\n", - "step: 600, loss: 109.713669, accuracy: 0.898438\n", - "step: 700, loss: 90.397217, accuracy: 0.906250\n", - "step: 800, loss: 104.545380, accuracy: 0.894531\n", - "step: 900, loss: 94.204697, accuracy: 0.890625\n", - "step: 1000, loss: 81.660645, accuracy: 0.906250\n", - "step: 1100, loss: 81.237137, accuracy: 0.902344\n", - "step: 1200, loss: 65.776703, accuracy: 0.925781\n", - "step: 1300, loss: 94.195862, accuracy: 0.910156\n", - "step: 1400, loss: 79.425507, accuracy: 0.917969\n", - "step: 1500, loss: 93.508163, accuracy: 0.914062\n", - "step: 1600, loss: 88.912506, accuracy: 0.917969\n", - "step: 1700, loss: 79.033607, accuracy: 0.929688\n", - "step: 1800, loss: 65.788315, accuracy: 0.898438\n", - "step: 1900, loss: 73.462387, accuracy: 0.937500\n", - "step: 2000, loss: 59.309540, accuracy: 0.917969\n", - "step: 2100, loss: 67.014008, accuracy: 0.917969\n", - "step: 2200, loss: 48.297115, accuracy: 0.949219\n", - "step: 2300, loss: 64.523148, accuracy: 0.910156\n", - "step: 2400, loss: 72.989517, accuracy: 0.925781\n", - "step: 2500, loss: 57.588585, accuracy: 0.929688\n", - "step: 2600, loss: 44.957100, accuracy: 0.960938\n", - "step: 2700, loss: 59.788242, accuracy: 0.937500\n", - "step: 2800, loss: 63.581337, accuracy: 0.937500\n", - "step: 2900, loss: 53.471252, accuracy: 0.941406\n", - "step: 3000, loss: 43.869728, accuracy: 0.949219\n" + "step: 100, loss: 571.445923, accuracy: 0.222656\n", + "step: 200, loss: 405.567535, accuracy: 0.488281\n", + "step: 300, loss: 252.089172, accuracy: 0.660156\n", + "step: 400, loss: 192.252136, accuracy: 0.792969\n", + "step: 500, loss: 129.173553, accuracy: 0.855469\n", + "step: 600, loss: 125.191071, accuracy: 0.859375\n", + "step: 700, loss: 103.346634, accuracy: 0.890625\n", + "step: 800, loss: 120.199402, accuracy: 0.871094\n", + "step: 900, loss: 95.674088, accuracy: 0.890625\n", + "step: 1000, loss: 113.775406, accuracy: 0.878906\n", + "step: 1100, loss: 68.457413, accuracy: 0.941406\n", + "step: 1200, loss: 80.773163, accuracy: 0.914062\n", + "step: 1300, loss: 85.862785, accuracy: 0.902344\n", + "step: 1400, loss: 63.480415, accuracy: 0.949219\n", + "step: 1500, loss: 77.139435, accuracy: 0.910156\n", + "step: 1600, loss: 88.129692, accuracy: 0.933594\n", + "step: 1700, loss: 92.199730, accuracy: 0.906250\n", + "step: 1800, loss: 90.150421, accuracy: 0.886719\n", + "step: 1900, loss: 48.567772, accuracy: 0.949219\n", + "step: 2000, loss: 54.002838, accuracy: 0.941406\n", + "step: 2100, loss: 58.536209, accuracy: 0.933594\n", + "step: 2200, loss: 47.156784, accuracy: 0.949219\n", + "step: 2300, loss: 55.344498, accuracy: 0.949219\n", + "step: 2400, loss: 70.956612, accuracy: 0.925781\n", + "step: 2500, loss: 76.179062, accuracy: 0.917969\n", + "step: 2600, loss: 44.956696, accuracy: 0.929688\n", + "step: 2700, loss: 56.581280, accuracy: 0.941406\n", + "step: 2800, loss: 57.775612, accuracy: 0.937500\n", + "step: 2900, loss: 46.005424, accuracy: 0.960938\n", + "step: 3000, loss: 51.832504, accuracy: 0.953125\n" ] } ], @@ -253,7 +253,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test Accuracy: 0.936800\n" + "Test Accuracy: 0.937600\n" ] } ], @@ -280,12 +280,14 @@ "outputs": [ { "data": { - "image/png": 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -314,12 +318,14 @@ }, { "data": { - "image/png": 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"text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -376,25 +386,32 @@ " plt.show()\n", " print(\"Model prediction: %i\" % np.argmax(predictions.numpy()[i]))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" + "pygments_lexer": "ipython3", + "version": "3.8.0" } }, "nbformat": 4,