{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# XOR Multiple Inputs/Targets\n", "\n", "This notebook explores networks with multiple input and output banks." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n", "ConX, version 3.7.5\n" ] } ], "source": [ "import conx as cx" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "net = cx.Network(\"XOR Network\", 2, 3, 1, activation=\"sigmoid\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "net.compile(loss='mean_squared_error', optimizer='sgd', lr=0.3, momentum=0.9)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "XOR = [\n", " ([0, 0], [0], \"0\"),\n", " ([0, 1], [1], \"1\"),\n", " ([1, 0], [1], \"1\"),\n", " ([1, 1], [0], \"0\")\n", "]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "XOR Network Dataset:\n", "Patterns Shape Range \n", "=================================================================\n", "inputs (2,) (0.0, 1.0) \n", "targets (1,) (0.0, 1.0) \n", "=================================================================\n", "Total patterns: 4\n", " Training patterns: 4\n", " Testing patterns: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "net.dataset.load(XOR)\n", "net.dataset.summary()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c8c2a401e1a8446fb578726caab38db9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Dashboard(children=(Accordion(children=(HBox(children=(VBox(children=(Select(description='Dataset:', index=1, …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dash = net.dashboard()\n", "dash" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.4066244959831238]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate([0, 1])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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"net.train(epochs=1000, accuracy=1, report_rate=25, record=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0f990951644e46dd8d3d024917a3b5a6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SequenceViewer(children=(VBox(children=(HBox(children=(IntSlider(value=0, continuous_update=False, description…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "zeros = net.dataset.inputs.select(lambda i,ds: ds.labels[i] == \"0\")\n", "ones = net.dataset.inputs.select(lambda i,ds: ds.labels[i] == \"1\")\n", "\n", "net.playback(lambda net,epoch: (\n", " net.plot_activation_map(scatter=[[\"0\", zeros], [\"1\", ones]],\n", " symbols={\"0\": \"ko\", \"1\": \"k+\"},\n", " title=\"Epoch %s\" % epoch,\n", " format=\"image\"),\n", " net.plot('all', end=epoch+1, format=\"image\")))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to remember to reset the network's weights to the last epoch of training, so that subsequent interactions with the network reflect its learned knowledge." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "net.set_weights_from_history(-1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "states = [net.propagate_to(\"hidden\", pattern) for pattern in net.dataset.inputs]\n", "pca = cx.PCA(states)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4e4a4dcb090444f892e234398e9e05e5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SequenceViewer(children=(VBox(children=(HBox(children=(IntSlider(value=0, continuous_update=False, description…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/svg+xml": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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" \"1 (correct)\": \"ro\",\n", " \"1 (wrong)\": \"rx\",\n", "}\n", "net.playback(lambda net,epoch: cx.scatter(**pca.transform_network_bank(net, \"hidden\"),\n", " symbols=symbols,\n", " format='svg'))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "net.set_weights_from_history(-1)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.0, 1.0]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate_to(\"input\", [0, 1])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.1825655698776245]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate([0.5, 0.5])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.07512849569320679, 0.9955328702926636, 0.9639556407928467]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate_to(\"hidden\", [1, 0])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.09972702711820602]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate_to(\"output\", [1, 1])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.25, 0.25]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate_to(\"input\", [0.25, 0.25])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.09972702711820602]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate_from(\"input\", [1.0, 1.0])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[8.784712554188445e-05]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate_from(\"hidden\", [1.0, 0.0, -1.0])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "net2 = cx.Network(\"XOR2 Network\")\n", "\n", "net2.add(cx.Layer(\"input1\", 1),\n", " cx.Layer(\"input2\", 1),\n", " cx.Layer(\"hidden1\", 10, activation=\"sigmoid\"),\n", " cx.Layer(\"hidden2\", 10, activation=\"sigmoid\"),\n", " cx.Layer(\"shared-hidden\", 5, activation=\"sigmoid\"),\n", " cx.Layer(\"output1\", 1, activation=\"sigmoid\"),\n", " cx.Layer(\"output2\", 1, activation=\"sigmoid\"))\n", "\n", "net2.connect(\"input1\", \"hidden1\")\n", "net2.connect(\"input2\", \"hidden2\")\n", "net2.connect(\"hidden1\", \"shared-hidden\")\n", "net2.connect(\"hidden2\", \"shared-hidden\")\n", "net2.connect(\"shared-hidden\", \"output1\")\n", "net2.connect(\"shared-hidden\", \"output2\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: network is uncompiled; activations cannot be visualized\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " Layer: output1 (output)\n", " output range: (0, 1)\n", " shape = (1,)\n", " Keras class = Dense\n", " activation = sigmoidoutput1Layer: output2 (output)\n", " output range: (0, 1)\n", " shape = (1,)\n", " Keras class = Dense\n", " activation = sigmoidoutput2Layer: shared-hidden (hidden)\n", " output range: (0, 1)\n", " shape = (5,)\n", " Keras class = Dense\n", " activation = sigmoidshared-hiddenLayer: hidden1 (hidden)\n", " output range: (0, 1)\n", " shape = (10,)\n", " Keras class = Dense\n", " activation = sigmoidhidden1Layer: hidden2 (hidden)\n", " output range: (0, 1)\n", " shape = (10,)\n", " Keras class = Dense\n", " activation = sigmoidhidden2Layer: input1 (input)\n", " output range: (-Infinity, +Infinity)\n", " shape = (1,)\n", " Keras class = Inputinput1Layer: input2 (input)\n", " output range: (-Infinity, +Infinity)\n", " shape = (1,)\n", " Keras class = Inputinput2XOR2 Network" ], "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net2.picture()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net2.layers[2].incoming_connections" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "net2.compile(loss='mean_squared_error', optimizer='SGD', lr=0.3, momentum=0.9)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "303030e64ab145568b1fa0f19949ed83", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Dashboard(children=(Accordion(children=(HBox(children=(VBox(children=(Select(description='Dataset:', index=1, …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net2.config[\"hspace\"] = 200\n", "dash = net2.dashboard()\n", "dash" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.541100800037384,\n", " 0.3278562128543854,\n", " 0.3921108841896057,\n", " 0.42058151960372925,\n", " 0.41650843620300293,\n", " 0.4361613690853119,\n", " 0.36962926387786865,\n", " 0.4186179041862488,\n", " 0.44622373580932617,\n", " 0.5558428764343262]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net2.propagate_to(\"hidden1\", [[1], [1]])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[0.5255315899848938], [0.24715031683444977]]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net2.propagate([[1], [1]])" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "XOR2 = [\n", " ([[0],[0]], [[0],[0]]),\n", " ([[0],[1]], [[1],[1]]),\n", " ([[1],[0]], [[1],[1]]),\n", " ([[1],[1]], [[0],[0]])\n", "]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "net2.dataset.load(XOR2)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[[-0.6272611618041992,\n", " 0.3946005702018738,\n", " 0.6141403317451477,\n", " 0.6681897044181824,\n", " -0.4868648052215576,\n", " -0.5547021627426147,\n", " 0.1374884843826294,\n", " -0.22490054368972778,\n", " -0.12803512811660767,\n", " -0.7175639867782593]],\n", " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net2.get_weights(\"hidden2\")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[0.5255315899848938], [0.24715031683444977]]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net2.propagate([[1], [1]])" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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\n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import time\n", "net2.reset()\n", "for i in range(20):\n", " (epoch_count, results) = net2.train(epochs=100, verbose=0, report_rate=25)\n", " for index in range(4):\n", " net2.propagate(XOR2[index][0])\n", " time.sleep(0.1)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "========================================================\n", " | Training | output1 | output2 \n", "Epochs | Error | acc | acc \n", "------ | --------- | --------- | --------- \n", "# 915 | 0.01573 | 1.00000 | 1.00000 \n" ] } ], "source": [ "net2.reset()\n", "net2.train(epochs=2000, accuracy=1.0, report_rate=25)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[0.6343947649002075], [0.5863332]]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net2.propagate_from(\"shared-hidden\", [0.0] * 5)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.11640828102827072,\n", " 0.8285501003265381,\n", " 0.8238552808761597,\n", " 0.7559378743171692,\n", " 0.7736209034919739,\n", " 0.7370111346244812,\n", " 0.8081612586975098,\n", " 0.27545997500419617,\n", " 0.8407012820243835,\n", " 0.20506168901920319]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net2.propagate_to(\"hidden1\", [[1], [1]])" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "net2.dataset.slice(2)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No training required: accuracy already to desired value\n", "Training dataset status:\n", " | Training | output1 | output2 \n", "Epochs | Error | acc | acc \n", "------ | --------- | --------- | --------- \n", "# 2000 | 0.00066 | 1.00000 | 1.00000 \n" ] } ], "source": [ "net2.train(epochs=2000, accuracy=1.0, report_rate=25)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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