{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gridfonts\n", "\n", "This notebook explores the datasets \"gridfonts\" and \"figure-ground-a\" based on Douglas Hofstadter and colleagues [Letter Spririt](https://cogsci.indiana.edu/letterspirit.html) project. See also [gridfonts](https://cogsci.indiana.edu/gridfonts.html).\n", "\n", "This data was used in both Gary McGraw's and Douglas Blank's theses to train neural networks. See section 6.3.2 of [McGraw's thesis](http://goosie.cogsci.indiana.edu/farg/mcgrawg/thesis.html), and Blank's thesis [Learning to See Analogies: a Connectionist Exploration](https://repository.brynmawr.edu/compsci_pubs/78/).\n", "\n", "* `figure-ground-a` is composed of letter a's \n", "* `gridfont` is composed of entire alphabets" ] }, { "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": "markdown", "metadata": {}, "source": [ "## Letterpart Analogies" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we download and load the figure_ground_a dataset:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "ds = cx.Dataset.get(\"figure_ground_a\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Figure-Ground A:\n", "Patterns Shape Range \n", "=================================================================\n", "inputs (153,) (0, 1) \n", "targets [(153,), (153,)] [(0, 1), (0, 1)] \n", "=================================================================\n", "Total patterns: 229\n", " Training patterns: 229\n", " Testing patterns: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "ds.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figure_ground_a database is composed of 229 a's where:\n", "\n", "* input - a 153 integer \"picture\" of an \"a\" representing 17 rows and 9 columns\n", "* targets - two 153 pictures, representing the \"brim\" of the letter a, and the \"body\"\n", "\n", "Let's display the letters in a graphical manner:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def display_letter_parts(letter, brim, body):\n", " print(\"Letter Brim Body\")\n", " for row in range(17):\n", " for col in range(9):\n", " print(\".\" if letter[row * 9 + col] == 0 else \"X\", end=\"\")\n", " print(\" \", end=\"\")\n", " for col in range(9):\n", " print(\".\" if brim[row * 9 + col] == 0 else \"X\", end=\"\")\n", " print(\" \", end=\"\")\n", " for col in range(9):\n", " print(\".\" if body[row * 9 + col] == 0 else \"X\", end=\"\")\n", " print()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Letter Brim Body\n", "......... ......... .........\n", "......... ......... .........\n", "......... ......... .........\n", "......... ......... .........\n", "....XXXXX ....X.... ....XXXXX\n", "...XX...X ...X..... ....X...X\n", "..X.X...X ..X...... ....X...X\n", ".X..X...X .X....... ....X...X\n", "X...X...X X........ ....X...X\n", "...X...X. ......... ...X...X.\n", "..X...X.. ......... ..X...X..\n", ".X...X... ......... .X...X...\n", "XXXXX.... ......... XXXXX....\n", "......... ......... .........\n", "......... ......... .........\n", "......... ......... .........\n", "......... ......... .........\n" ] } ], "source": [ "display_letter_parts(ds.inputs[0], ds.targets[0][0], ds.targets[0][1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's design some networks that attempt to replicate the brim and body given the original gridfont letter.\n", "\n", "First, let's change the format of the dataset input so that we can treat the input as a 2D 17 x 9 image rather than merely a vector of 153 values. We actually make it a 3D shape, the third dimension representing color (which it doesn't have)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "ds.inputs.reshape(0, (17, 9, 1))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(17, 9, 1)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cx.shape(ds.inputs[0])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFoAAACqCAIAAACiWM9MAAABGklEQVR4nO3bMQrDMBAAQTv4/1926iUgF8IoTmb6ILFccRhl2wAAAAAAAAAAAABg2/ZVB5/nuerogdfqC3wXOUKOkCPkCDlCjpAj5Igbt9KZvXPf77rY+FamI+QIOUKOkCPkCDlCjpAjjpkfjze88WbpW+kDyBFyhBwhR8gRcoQcIUdcbKUze+cTmY6QI+QIOUKOkCPkCDlCjjj+be8cMx0hR8gRcoQcIUfIEXKEHLHsXemql6Pjc01HyBFyhBwhR8gRcoQcIUdMrYZP3DvHTEfIEXKEHCFHyBFyhBwhR1wscL+3d46ZjpAj5Ag5Qo6QI+QIOUKO2L/zv/Cr3rOajpAj5Ag5Qo6QI+QIOUIOAAAAAAAAAAAAAPjwBmT6Kqzw7J4DAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cx.array_to_image(ds.inputs[0], scale=10)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFoAAACqCAIAAACiWM9MAAAAvUlEQVR4nO3WMQ7CMBQFQZv739nUKyFTIORAZtoUjla/eGMAAAAAAAAAAAAAwBjz1MNrrc3XOc/82OPIq5clR8gRcoQcIUfIEXKEHPHF8XfN3bnnOkKOkCPkCDlCjpAj5Ag54qNp+Iu7c891hBwhR8gRcoQcIUfIEXLEm+H4f7tzz3WEHCFHyBFyhBwhR8gRcsS82+7ccx0hR8gRcoQcIUfIEXKEHAAAAAAAAAAAAAAAAAAAAAAAAAAAAC89AYC7D2BZzfX5AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cx.array_to_image(ds.targets[0][0], scale=10, shape=(17,9))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFoAAACqCAIAAACiWM9MAAABC0lEQVR4nO3cMQ7CMBAAQRvx/y+HehVkChQ5oJkP2FpdcYWTMQAAAAAAAAAAAABgjLnr4OM4dh298Nh9gXuRI+QIOUKOkCPkCDlCjnjuvsB7c161Lq+3YdMRcoQcIUfIEXKEHCFHyBFyhBwhR8gRcoQcIUfIEXKEHCFHyBFyhBwhR8gRcoQcIUfIEXKEHCFHyBFyhBwhR8gRcoQcceHXTusXnLtejq7PNR0hR8gRcoQcIUfIEXKEHPHVaviLe+ea6Qg5Qo6QI+QIOUKOkCPkiA8L3P/tnWumI+QIOUKOkCPkCDlCjpAj5j3/G3rd3rlmOkKOkCPkCDlCjpAj5Ag5AAAAAAAAAAAAAODkBVWSG6/489I8AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cx.array_to_image(ds.targets[0][1], scale=10, shape=(17,9))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now use a Convolutional Layer to better process the letter \"picture\". Notice that we flatten the output of the Convolutional layer to bring the output back into a single dimension." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "net = cx.Network(\"Letterpart Analogies\")\n", "net.add(cx.Layer(\"input\", (17, 9, 1)))\n", "net.add(cx.Conv2DLayer(\"conv\", 2, (2,2)))\n", "net.add(cx.FlattenLayer(\"flatten\"))\n", "net.add(cx.Layer(\"hidden\", 50, activation=\"relu\"))\n", "net.add(cx.Layer(\"brim\", 153, vshape=(17, 9), activation=\"sigmoid\"))\n", "net.add(cx.Layer(\"body\", 153, vshape=(17, 9), activation=\"sigmoid\"))\n", "\n", "net.connect(\"input\", \"conv\")\n", "net.connect(\"conv\", \"flatten\")\n", "net.connect(\"flatten\", \"hidden\")\n", "net.connect(\"hidden\", \"brim\")\n", "net.connect(\"hidden\", \"body\")\n", "net.compile(error=\"mse\", optimizer=\"adam\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We associate the dataset to the network, and save 10 pictures for testing." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "net.set_dataset(ds)\n", "net.dataset.split(10)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1b9b812b202249b9a9bc3b59be182d62", "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": [ "Now, we are ready to train the network to replicate the letter parts." ] }, { "cell_type": "code", "execution_count": 14, "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", 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batch_size=128, report_rate=100)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net.plot(\".*acc\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " Layer: brim (output)\n", " output range: (0, 1)\n", " shape = (153,)\n", " Keras class = Dense\n", " activation = sigmoidbrimLayer: body (output)\n", " output range: (0, 1)\n", " shape = (153,)\n", " Keras class = Dense\n", " activation = sigmoidbodyLayer: hidden (hidden)\n", " output range: (0, +Infinity)\n", " shape = (50,)\n", " Keras class = Dense\n", " activation = reluhiddenLayer: flatten (hidden)\n", " output range: (-Infinity, +Infinity)\n", " Keras class = FlattenflattenLayer: conv (hidden)\n", " output range: (-Infinity, +Infinity)\n", " Keras class = Conv2Dconv20Layer: input (input)\n", " output range: (0, 1)\n", " shape = (17, 9, 1)\n", " Keras class = Inputinput10Letterpart Analogies" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.picture()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "results = net.train_one(ds.inputs[8], ds.targets[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gridfonts - leter recognition\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "ds = cx.Dataset.get(\"gridfonts\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Gridfonts:\n", "Patterns Shape Range \n", "=================================================================\n", "inputs (25, 9) (0.0, 1.0) \n", "targets (25, 9) (0.0, 1.0) \n", "=================================================================\n", "Total patterns: 7462\n", " Training patterns: 7462\n", " Testing patterns: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "ds.summary()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "net = cx.Network(\"Gridfonts\")\n", "net.add(cx.Layer(\"inputs\", (25,9)))\n", "net.add(cx.FlattenLayer(\"flatten\"))\n", "net.add(cx.Layer(\"hiddens\", 20, activation=\"sigmoid\"))\n", "net.add(cx.Layer(\"outputs\", (25, 9), activation=\"relu\"))\n", "net.connect()\n", "net.compile(error=\"mse\", optimizer=\"adam\")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: network 'Gridfonts' target bank #0 has a multi-dimensional shape; is this correct?\n" ] } ], "source": [ "net.set_dataset(ds)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " Layer: outputs (output)\n", " output range: (0, +Infinity)\n", " shape = (25, 9)\n", " Keras class = Dense\n", " activation = reluoutputsLayer: hiddens (hidden)\n", " output range: (0, 1)\n", " shape = (20,)\n", " Keras class = Dense\n", " activation = sigmoidhiddensLayer: flatten (hidden)\n", " output range: (0.0, 1.0)\n", " Keras class = FlattenflattenLayer: inputs (input)\n", " output range: (0.0, 1.0)\n", " shape = (25, 9)\n", " Keras class = InputinputsGridfonts" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "a\n" ] } ], "source": [ "display(net)\n", "output = net.propagate(ds.inputs[26])\n", "print(ds.labels[26])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "def display_letter(letter):\n", " print(\"Letter\")\n", " for row in range(25):\n", " for col in range(9):\n", " print(\".\" if letter[row][col] == 0 else \"X\", end=\"\")\n", " print()" ] }, { "cell_type": 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\n \n \n \n \n GridfontsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoiderrorsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoiderrorsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbodyLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbrimWeights from hidden to brim\n brim/kernel has shape (50, 153)\n brim/bias has shape (153,)Weights from hidden to body\n body/kernel has shape (50, 153)\n body/bias has shape (153,)Layer: hidden (hidden)\n shape = (50,)\n Keras class = Dense\n activation = reluhiddenWeights from flatten to hidden\n hidden/kernel has shape (256, 50)\n hidden/bias has shape (50,)Layer: flatten (hidden)\n Keras class = FlattenflattenWeights from conv to flattenLayer: conv (hidden)\n Keras class = Conv2Dconv20Weights from input to conv\n conv/kernel has shape (2, 2, 1, 2)\n conv/bias has shape (2,)Layer: input (input)\n shape = (17, 9, 1)\n Keras class = Inputinput10

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"model_module_version": "1.4.0", "model_name": "CheckboxModel", "state": { "description": "Visible", "disabled": false, "layout": "IPY_MODEL_bc24ac7d1367441990dba741aa62d88d", "style": "IPY_MODEL_977fa74dada8413c97a6165ea0368eca", "value": true } }, "4ee53b2bdc8b4d7ca8f454e80e84536c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_4e3420b44e674f0f8ff25b98541bfc4f", "style": "IPY_MODEL_1a3f6c549c1f49239a3322c65f5288ff", "value": "

\n \n \n \n \n GridfontsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoiderrorsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoiderrorsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbodyLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbrimWeights from hidden to brim\n brim/kernel has shape (50, 153)\n brim/bias has shape (153,)Weights from hidden to body\n body/kernel has shape (50, 153)\n body/bias has shape (153,)Layer: hidden (hidden)\n shape = (50,)\n Keras class = Dense\n activation = reluhiddenWeights from flatten to hidden\n hidden/kernel has shape (256, 50)\n hidden/bias has shape (50,)Layer: flatten (hidden)\n Keras class = FlattenflattenWeights from conv to flattenLayer: conv (hidden)\n Keras class = Conv2Dconv20Weights from input to conv\n conv/kernel has shape (2, 2, 1, 2)\n conv/bias has shape (2,)Layer: input (input)\n shape = (17, 9, 1)\n Keras class = Inputinput10

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"model_name": "FloatTextModel", "state": { "description": "Rightmost color maps to:", "layout": "IPY_MODEL_5243cde759e849d796b1a5dbd86f116c", "step": null, "style": "IPY_MODEL_92704a9f8a3c4d50be1f84361b8b3461", "value": 1 } }, "4fe875a409fb4fb8b7c21d7d82f38c75": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "CheckboxModel", "state": { "description": "Errors", "disabled": false, "layout": "IPY_MODEL_fe8e058fd0c046b0af6f3d972ff42b21", "style": "IPY_MODEL_23786577017148b7b0c2c1b9e6372f58", "value": true } }, "4fed6187f6f64160a286a7d94910e12a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "50069b4146f046579160973ae8dcd193": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntTextModel", "state": { "description": "Vertical space between layers:", "layout": "IPY_MODEL_c78cd5d6e2b4455bb6f91c0cef62a6ff", "step": 1, "style": "IPY_MODEL_074ded8444744e30bc6ff3225c624dea", "value": 30 } }, "5009895edd7d45c692bee59ddfb379b6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "CheckboxModel", "state": { "description": "Visible", "disabled": false, "layout": "IPY_MODEL_fba041d041fa4ff788d6d0ea12fa320b", "style": "IPY_MODEL_5ffaf1b7e2f44f2a82e28a4fc544efdc", "value": true } }, "500f2ca33e084d01a7efa6b8ca96c931": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "LabelModel", "state": { "layout": "IPY_MODEL_4de7abf1bed143ac8b52b90c543fad07", "style": "IPY_MODEL_aace743db4da4218b7e3f4813c7b4b68", "value": "of 219" } }, "50197293ae7f4ab89040fa1c4a9c0bc7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntSliderModel", "state": { "continuous_update": false, "description": "Dataset index", "layout": "IPY_MODEL_afc415dd7b794d4d86229d40749f39f2", "max": 218, "style": 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"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "FloatSliderModel", "state": { "continuous_update": false, "description": "Zoom", "layout": "IPY_MODEL_e7c4fe5bc01c4be69131eba9b2c9751a", "max": 3, "min": 0.5, "step": 0.1, "style": "IPY_MODEL_e2ba38d9cafe495ab821c3b1155b0170", "value": 1 } }, "68ba171841f444e3a4ad209b5ac7ae85": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "FloatTextModel", "state": { "description": "Rightmost color maps to:", "layout": "IPY_MODEL_d301d2e7fad9417da722495824a02f53", "step": null, "style": "IPY_MODEL_259ffa3cb8064e4a844252d691063d40", "value": 1 } }, "68dce6959f724f16a75f2398f7ae5f59": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "ButtonModel", "state": { "icon": "backward", "layout": "IPY_MODEL_6dcedb007ad543309dbe81def30f29dd", "style": "IPY_MODEL_d80ce544c1da41eaa29feb715a43279e" } }, 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\n \n \n \n \n Letterpart AnalogiesLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbodyLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbrimWeights from hidden to brim\n brim_2/kernel:0 has shape (50, 153)\n brim_2/bias:0 has shape (153,)Weights from hidden to body\n body_2/kernel:0 has shape (50, 153)\n body_2/bias:0 has shape (153,)Layer: hidden (hidden)\n shape = (50,)\n Keras class = Dense\n activation = reluhiddenWeights from flatten to hidden\n hidden_2/kernel:0 has shape (256, 50)\n hidden_2/bias:0 has shape (50,)Layer: flatten (hidden)\n Keras class = FlattenflattenWeights from conv to flattenLayer: conv (hidden)\n Keras class = Conv2Dconv20Weights from input to conv\n conv_2/kernel:0 has shape (2, 2, 1, 2)\n conv_2/bias:0 has shape (2,)Layer: input (input)\n shape = (17, 9, 1)\n Keras class = Inputinput10

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\n \n \n \n \n GridfontsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbodyLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbrimWeights from hidden to brim\n brim/kernel has shape (50, 153)\n brim/bias has shape (153,)Weights from hidden to body\n body/kernel has shape (50, 153)\n body/bias has shape (153,)Layer: hidden (hidden)\n shape = (50,)\n Keras class = Dense\n activation = reluhiddenWeights from flatten to hidden\n hidden/kernel has shape (256, 50)\n hidden/bias has shape (50,)Layer: flatten (hidden)\n Keras class = FlattenflattenWeights from conv to flattenLayer: conv (hidden)\n Keras class = Conv2Dconv20Weights from input to conv\n conv/kernel has shape (2, 2, 1, 2)\n conv/bias has shape (2,)Layer: input (input)\n shape = (17, 9, 1)\n Keras class = Inputinput10

" } }, "6bd98b0e9e0a4b6ba8f07840ecb26230": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "initial" } }, "6bde7b5ef6ef40bf8925320931d65b0d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_7b2007f165504151942fc8b2d4326645", "style": "IPY_MODEL_240bf4052cf74ae9a1a04ac538ccbf61", "value": "

\n \n \n \n \n GridfontsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbodyLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbrimWeights from hidden to brim\n brim/kernel has shape (50, 153)\n brim/bias has shape (153,)Weights from hidden to body\n body/kernel has shape (50, 153)\n body/bias has shape (153,)Layer: hidden (hidden)\n shape = (50,)\n Keras class = Dense\n activation = reluhiddenWeights from flatten to hidden\n hidden/kernel has shape (256, 50)\n hidden/bias has shape (50,)Layer: flatten (hidden)\n Keras class = FlattenflattenWeights from conv to flattenLayer: conv (hidden)\n Keras class = Conv2Dconv20Weights from input to conv\n conv/kernel has shape (2, 2, 1, 2)\n conv/bias has shape (2,)Layer: input (input)\n shape = (17, 9, 1)\n Keras class = Inputinput10

" } }, "6bea7384480543419fb34a1547169a72": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "initial" } }, "6c01f75cfea44fa09f5bd80caaa85570": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "6c102f851a7945bc8ce51d864100f6f8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "initial" } }, "6c1dc2a34acb411982cc6157e9670b1d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "ButtonStyleModel", "state": {} }, "6c428daa24f54021ac6e0ef9c7fbc7a6": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.1.0", "model_name": "OutputModel", "state": { "_model_module_version": "1.1.0", "_view_module_version": "1.1.0", "layout": 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\n \n \n \n \n GridfontsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoiderrorsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoiderrorsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbodyLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbrimWeights from hidden to brim\n brim/kernel has shape (50, 153)\n brim/bias has shape (153,)Weights from hidden to body\n body/kernel has shape (50, 153)\n body/bias has shape (153,)Layer: hidden (hidden)\n shape = (50,)\n Keras class = Dense\n activation = reluhiddenWeights from flatten to hidden\n hidden/kernel has shape (256, 50)\n hidden/bias has shape (50,)Layer: flatten (hidden)\n Keras class = FlattenflattenWeights from conv to flattenLayer: conv (hidden)\n Keras class = Conv2Dconv20Weights from input to conv\n conv/kernel has shape (2, 2, 1, 2)\n conv/bias has shape (2,)Layer: input (input)\n shape = (17, 9, 1)\n Keras class = Inputinput10

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\n \n \n \n \n GridfontsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbodyLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbrimWeights from hidden to brim\n brim/kernel has shape (50, 153)\n brim/bias has shape (153,)Weights from hidden to body\n body/kernel has shape (50, 153)\n body/bias has shape (153,)Layer: hidden (hidden)\n shape = (50,)\n Keras class = Dense\n activation = reluhiddenWeights from input to hidden\n hidden/kernel has shape (153, 50)\n hidden/bias has shape (50,)Layer: input (input)\n shape = (153,)\n Keras class = Inputinput

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"layout": "IPY_MODEL_1b438c12e2c041039aaeb100ea16c38a", "step": 1, "style": "IPY_MODEL_d85f1d27fc924f9b936863656a341eb2", "value": 150 } }, "81bb881819b446e49f4e11ebf7bcc625": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "CheckboxModel", "state": { "description": "Show Targets", "disabled": false, "layout": "IPY_MODEL_fe8e058fd0c046b0af6f3d972ff42b21", "style": "IPY_MODEL_c568f05734d24f108a17210593f36171", "value": true } }, "81c8d8b3ca6b479e9cc5d0b19b5fafdf": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "81cdaf02295e4001922215e55c87609b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "AccordionModel", "state": { "_titles": { "0": "Gridfonts" }, "children": [ "IPY_MODEL_4a9d043266a54ceab4d64a95f4f20fe6" ], "layout": "IPY_MODEL_379afc85999c4c87b2200751de5f5dce", "selected_index": null } }, 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"description": "Leftmost color maps to:", "layout": "IPY_MODEL_c8f4ab7ab1f149e5a76fcf0999d29979", "step": null, "style": "IPY_MODEL_7563226581d94684a95e02fbd9af82fd", "value": -1 } }, "826625e88749460ebe9a1b458d3ccded": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_ca23057f7be243ba8edf1ec698c22411", "style": "IPY_MODEL_0c550a2b6154403793d1e1a692fe95a5", "value": "

\n \n \n \n \n GridfontsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoiderrorsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoiderrorsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbodyLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbrimWeights from hidden to brim\n brim/kernel has shape (50, 153)\n brim/bias has shape (153,)Weights from hidden to body\n body/kernel has shape (50, 153)\n body/bias has shape (153,)Layer: hidden (hidden)\n shape = (50,)\n Keras class = Dense\n activation = reluhiddenWeights from flatten to hidden\n hidden/kernel has shape (256, 50)\n hidden/bias has shape (50,)Layer: flatten (hidden)\n Keras class = FlattenflattenWeights from conv to flattenLayer: conv (hidden)\n Keras class = Conv2Dconv20Weights from input to conv\n conv/kernel has shape (2, 2, 1, 2)\n conv/bias has shape (2,)Layer: input (input)\n shape = (17, 9, 1)\n Keras class = Inputinput10

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\n \n \n \n \n GridfontsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidtargetsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoiderrorsLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoiderrorsLayer: body (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbodyLayer: brim (output)\n shape = (153,)\n Keras class = Dense\n activation = sigmoidbrimWeights from hidden to brim\n brim/kernel has shape (50, 153)\n brim/bias has shape (153,)Weights from hidden to body\n body/kernel has shape (50, 153)\n body/bias has shape (153,)Layer: hidden (hidden)\n shape = (50,)\n Keras class = Dense\n activation = reluhiddenWeights from flatten to hidden\n hidden/kernel has shape (256, 50)\n hidden/bias has shape (50,)Layer: flatten (hidden)\n Keras class = FlattenflattenWeights from conv to flattenLayer: conv (hidden)\n Keras class = Conv2Dconv20Weights from input to conv\n conv/kernel has shape (2, 2, 1, 2)\n conv/bias has shape (2,)Layer: input (input)\n shape = (17, 9, 1)\n Keras class = Inputinput10

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