{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Learning\n", "\n", "The shallowest network is one that has no hidden layers at all. But this type of network can only solve one type of problem: those that are **linearly separable**. This notebook explores learning linearly and non-linearly separable datasets.\n", "\n", "## Linearly Separable" ] }, { "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\n", "import random" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's construct a fake linearly-separable dataset." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "count = 500\n", "\n", "positives = [(i/count, i/(count * 2) + random.random()/6) for i in range(count)]\n", "negatives = [(i/count, 0.3 + i/(count * 2) + random.random()/6) for i in range(count)]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \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": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cx.scatter([\n", " [\"Positive\", positives], \n", " [\"Negative\", negatives],\n", " ], \n", " symbols={\"Positive\": \"bo\", \"Negative\": \"ro\"})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make a dataset out of these two types:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "ds = cx.Dataset()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A dataset is composed of:\n", "\n", "* inputs - the (x,y)\n", "* targets - [0] or [1]\n", "* labels - a string representing the category" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "ds.load([(p, [ 1.0], \"Positive\") for p in positives] +\n", " [(n, [ 0.0], \"Negative\") for n in negatives])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although the patterns will be shuffled during training, we will also shuffle them here just to make it clear that as we view them that the target should be determined from the (x,y) values." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "ds.shuffle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will save 10% of the patterns for testing/validation:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "ds.split(.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally, we look at the dataset summary to make sure it looks right:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Unnamed Dataset:\n", "Patterns Shape Range \n", "=================================================================\n", "inputs (2,) (0.0, 0.998) \n", "targets (1,) (0.0, 1.0) \n", "=================================================================\n", "Total patterns: 1000\n", " Training patterns: 900\n", " Testing patterns: 100\n", "_________________________________________________________________\n" ] } ], "source": [ "ds.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are ready to design the network. This one is fairly easy: the inputs and outputs are determined by the dataset. We need only determine the error function, the optimizer, and the activation function on the output layer:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "net = cx.Network(\"Linearly Separable\", 2, 1, activation=\"sigmoid\")\n", "net.compile(error=\"mae\", optimizer=\"adam\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We associate the dataset with the network:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "net.set_dataset(ds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And let's use the dashboard to see the dataset and network:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f4cb19130b8549e3bb41163e2077f581", "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": [ "net.dashboard()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How complicated is this network?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input (InputLayer) (None, 2) 0 \n", "_________________________________________________________________\n", "output (Dense) (None, 1) 3 \n", "=================================================================\n", "Total params: 3\n", "Trainable params: 3\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "net.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can get a flat array of these weights with `net.to_array()`:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[-0.67281747, 0.81628263, 0.0]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.to_array()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before training, let's see how bad the network does with these random weights:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Linearly Separable:\n", "Training Data Results:\n", "----+--------------+--------+------+-----+-\n", " | | target | | | \n", " # | input | output | loss | acc | \n", "----+--------------+--------+------+-----+-\n", " 0 | [0.90, 0.83] | [0.00] | 0.52 | x | \n", " 1 | [0.91, 0.86] | [0.00] | 0.52 | x | \n", " 2 | [0.46, 0.65] | [0.00] | 0.56 | x | \n", " 3 | [0.58, 0.30] | [1.00] | 0.54 | x | \n", " 4 | [0.66, 0.42] | [1.00] | 0.53 | x | \n", " 5 | [0.92, 0.81] | [0.00] | 0.51 | x | \n", " 6 | [0.04, 0.06] | [1.00] | 0.49 | x | \n", " 7 | [0.19, 0.16] | [1.00] | 0.50 | x | \n", " 8 | [0.85, 0.56] | [1.00] | 0.53 | x | \n", " 9 | [0.28, 0.22] | [1.00] | 0.50 | x | \n", " 10 | [0.15, 0.50] | [0.00] | 0.58 | x | \n", " 11 | [0.87, 0.76] | [0.00] | 0.51 | x | \n", " 12 | [0.77, 0.51] | [1.00] | 0.53 | x | \n", " 13 | [0.93, 0.90] | [0.00] | 0.53 | x | \n", " 14 | [0.10, 0.44] | [0.00] | 0.57 | x | \n", "\n", "... skipping some rows ...\n", "\n", "----+--------------+--------+------+-----+-\n", "Correct: 0 | \n", "-----------------------------------------+-\n", " loss: 0.53\n", " acc: 0.00\n", "\n", "Testing Data Results:\n", "----+--------------+--------+------+-----+-\n", " | | target | | | \n", " # | input | output | loss | acc | \n", "----+--------------+--------+------+-----+-\n", "900 | [0.33, 0.61] | [0.00] | 0.57 | x | \n", "901 | [0.27, 0.30] | [1.00] | 0.49 | x | \n", "902 | [0.55, 0.43] | [1.00] | 0.51 | x | \n", "903 | [0.11, 0.12] | [1.00] | 0.49 | x | \n", "904 | [0.01, 0.06] | [1.00] | 0.49 | x | \n", "905 | [0.27, 0.51] | [0.00] | 0.56 | x | \n", "906 | [0.40, 0.29] | [1.00] | 0.51 | x | \n", "907 | [0.45, 0.64] | [0.00] | 0.55 | x | \n", "908 | [0.99, 0.80] | [0.00] | 0.50 | x | \n", "909 | [0.98, 0.60] | [1.00] | 0.54 | x | \n", "910 | [0.18, 0.12] | [1.00] | 0.51 | x | \n", "911 | [0.94, 0.55] | [1.00] | 0.55 | x | \n", "912 | [0.81, 0.74] | [0.00] | 0.52 | x | \n", "913 | [0.09, 0.38] | [0.00] | 0.56 | x | \n", "914 | [0.74, 0.68] | [0.00] | 0.52 | x | \n", "\n", "... skipping some rows ...\n", "\n", "----+--------------+--------+------+-----+-\n", "Correct: 0 | \n", "-----------------------------------------+-\n", " loss: 0.53\n", " acc: 0.00\n" ] } ], "source": [ "net.evaluate(tolerance=0.4, show=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also, we can use the `net.plot_activation_map()` method that Lisa introduced in the \"Getting Started with ConX\" notebook.\n", "\n", "Recall that the method will sample from the range of possible inputs of two units of an input layer, and will show you a \"heatmap\" of the resulting activation of one unit in any hidden or output layer. \n", "\n", "By default, it will use the first two units of a layer named \"input\" and will propagate to the first unit of a layer called \"output\". This matches our network exactly, so we can just use the defaults." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net.plot_activation_map(title=\"Before Training\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition, we can also put scatter plot on top of the activations, and give those points a legend by using the net.evaluate_and_label() method:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# net.evaluate_and_label(tolerance=0.4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This will return a list of tuples in the form:\n", "\n", "(\"LABEL (correct | wrong)\", INPUTS)\n", "\n", "like so:\n", "\n", "```python\n", "[('Negative (correct)',\n", " [[0.5239999890327454, 0.7271904349327087],\n", " ...\n", " [0.33000001311302185, 0.5292619466781616]]),\n", " ('Positive (wrong)',\n", " [[0.08399999886751175, 0.3982737064361572],\n", " ...\n", " [0.33000001311302185, 0.5292619466781616]]),\n", " ('Positive (correct)',\n", " [[0.08399999886751175, 0.3982737064361572],\n", " ...\n", " [0.33000001311302185, 0.5292619466781616]]),\n", " ('Negative (wrong)',\n", " [[0.08399999886751175, 0.3982737064361572],\n", " ...\n", " [0.014000000432133675, 0.05173352733254433]])]\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Putting that all together, with a dictionary of symbols, gives:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "symbols = {\n", " \"Positive (correct)\": \"w+\",\n", " \"Positive (wrong)\": \"k+\",\n", " \"Negative (correct)\": \"w_\",\n", " \"Negative (wrong)\": \"k_\",\n", "}\n", "\n", "net.plot_activation_map(scatter=net.evaluate_and_label(tolerance=0.4), \n", " symbols=symbols, title=\"Before Training\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, now we are ready to train the network:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "#net.delete()\n", "#net.reset()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if net.saved():\n", " net.load()\n", " net.plot_results()\n", "else:\n", " net.train(epochs=10000, accuracy=1.0, report_rate=50, \n", " tolerance=0.4, batch_size=len(net.dataset.train_inputs), \n", " plot=True, record=100, save=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can see the trained network on the training set:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net.plot_activation_map(scatter=net.evaluate_and_label(tolerance=0.4), \n", " symbols=symbols, title=\"After Training\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can see the weights after training:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3.086318, -6.2050633, 1.4618336]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.to_array()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To verify the network so see that it is computing the outputs as expected, we can manually do the math.\n", "\n", "That is, the sigmoid(sum of the inputs * weights + bias) should gives us the same output as net.propagate()." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "from conx.activations import sigmoid\n", "\n", "def output(x, y):\n", " wts = net.get_weights(\"output\")\n", " return sigmoid(x * wts[0][1][0] + y * wts[0][0][0] + wts[1][0])\n", "\n", "def ascii(f):\n", " return \"%4.1f\" % f" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use that to make our own ASCII heatmap:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.0 0.0 0.0 0.1 0.1 0.2 0.3 0.4 0.6 \n", " 0.0 0.0 0.0 0.1 0.1 0.2 0.3 0.5 0.6 \n", " 0.0 0.0 0.1 0.1 0.2 0.3 0.4 0.6 0.7 \n", " 0.0 0.0 0.1 0.1 0.2 0.3 0.5 0.6 0.8 \n", " 0.0 0.1 0.1 0.2 0.3 0.4 0.6 0.7 0.8 \n", " 0.0 0.1 0.1 0.2 0.3 0.5 0.6 0.8 0.9 \n", " 0.1 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 \n", " 0.1 0.1 0.2 0.3 0.5 0.6 0.8 0.9 0.9 \n", " 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 0.9 \n", " 0.1 0.2 0.3 0.5 0.6 0.8 0.9 0.9 1.0 \n", " 0.2 0.3 0.4 0.6 0.7 0.8 0.9 0.9 1.0 \n" ] } ], "source": [ "for y in cx.frange(0, 1.1, .1):\n", " for x in cx.frange(1.0, 0.1, -0.1):\n", " print(ascii(output(x, y)), end=\" \")\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, it would be very useful to see how learning works over time. For this, we can use the net.playback() method. It takes a function of the form:\n", "\n", "```python\n", "def function(network, epoch):\n", " return IMAGE\n", "```\n", "\n", "The function is given the network as it was at the time of EPOCH. That is, the network is transported back in time.\n", "\n", "We use a variation of the plot_activation_map:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "48ebc465156d45a7abdd4922c4677248", "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", " 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"net.set_weights_from_history(-1)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# net.movie(lambda net, epoch: net.plot_activation_map(title=\"Epoch %s\" % epoch, \n", "# scatter=net.evaluate_and_label(tolerance=0.4), \n", "# symbols=symbols, \n", "# format=\"image\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Non-Linearly Separable" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "import math" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def distance(x1, y1, x2, y2):\n", " return math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "negatives = []\n", "while len(negatives) < 500:\n", " x = random.random()\n", " y = random.random()\n", " d = distance(x, y, 0.5, 0.5)\n", " if d > 0.375 and d < 0.5:\n", " negatives.append([x, y])\n", "positives = []\n", "while len(positives) < 500:\n", " x = random.random()\n", " y = random.random()\n", " d = distance(x, y, 0.5, 0.5)\n", " if d < 0.25:\n", " positives.append([x, y])" ] }, { "cell_type": "code", "execution_count": 31, "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", " 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"execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " Layer: output (output)\n", " output range: (0, 1)\n", " shape = (1,)\n", " Keras class = Dense\n", " activation = sigmoidoutputLayer: hidden (hidden)\n", " output range: (0, 1)\n", " shape = (5,)\n", " Keras class = Dense\n", " activation = sigmoidhiddenLayer: input (input)\n", " output range: (-Infinity, +Infinity)\n", " shape = (2,)\n", " Keras class = InputinputNon-Linearly Separable" ], "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.picture()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "ds = cx.Dataset()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "ds.load([(p, [ 1.0], \"Positive\") for p in positives] +\n", " [(n, [ 0.0], \"Negative\") for n in negatives])" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "ds.shuffle()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "ds.split(.1)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "net.set_dataset(ds)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Non-Linearly Separable:\n", "Training Data Results:\n", " loss: 0.50\n", " acc: 0.51\n", "\n", "Testing Data Results:\n", " loss: 0.51\n", " acc: 0.45\n" ] } ], "source": [ "net.evaluate(tolerance=0.4)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "273ee8a510d64803a9a2816ef4cb98ff", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Dashboard(children=(Accordion(children=(HBox(children=(VBox(children=(Select(description='Dataset:', index=1, 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net.plot_activation_map(scatter=net.evaluate_and_label(), symbols=symbols, title=\"Before Training\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may want to either `net.reset()` or `net.retrain()` if the following cell doesn't complete with 100% accuracy. Calling `net.reset()` may be needed if the network has landed in a local maxima; `net.retrain()` may be necessary if the network just needs additional training." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# net.delete()\n", "# net.reset()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if net.saved():\n", " net.load()\n", " net.plot_results()\n", "else:\n", " net.train(epochs=10000, accuracy=1.0, report_rate=50, \n", " tolerance=0.4, batch_size=256, \n", " plot=True, record=100, save=True)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net.plot_activation_map(scatter=net.evaluate_and_label(), symbols=symbols, title=\"After Training\")" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", " 0.0 0.0 0.0 0.2 0.3 0.4 0.3 0.1 0.1 \n", " 0.0 0.1 0.2 0.7 0.9 1.0 0.9 0.7 0.2 \n", " 0.0 0.1 0.4 0.9 1.0 1.0 1.0 1.0 0.8 \n", " 0.0 0.1 0.4 0.9 1.0 1.0 1.0 1.0 0.8 \n", " 0.0 0.1 0.4 0.9 1.0 1.0 1.0 0.9 0.4 \n", " 0.0 0.1 0.3 0.8 0.9 0.9 0.8 0.3 0.0 \n", " 0.0 0.0 0.2 0.5 0.6 0.5 0.1 0.0 0.0 \n", " 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n" ] } ], "source": [ "for y in cx.frange(0, 1.1, .1):\n", " for x in cx.frange(1.0, 0.1, -0.1):\n", " print(ascii(net.propagate([x, y])[0]), end=\" \")\n", " print()" ] }, { "cell_type": "code", "execution_count": 46, 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\n \n \n \n \n Non-Linearly SeparableLayer: output (output)\n shape = (1,)\n Keras class = Dense\n activation = sigmoidoutputWeights from hidden2 to output\n output/kernel has shape (2, 1)\n output/bias has shape (1,)Layer: hidden2 (hidden)\n shape = (2,)\n Keras class = Dense\n activation = sigmoidhidden2Weights from hidden1 to hidden2\n hidden2/kernel has shape (5, 2)\n hidden2/bias has shape (2,)Layer: hidden1 (hidden)\n shape = (5,)\n Keras class = Dense\n activation = sigmoidhidden1Weights from input to hidden1\n hidden1/kernel has shape (2, 5)\n hidden1/bias has shape (5,)Layer: input (input)\n shape = (2,)\n Keras class = Inputinput

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