{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting Started with ConX\n", "\n", "## What is ConX?\n", "\n", "ConX is an accessible and powerful way to build and understand deep learning neural networks. Specifically, it sits on top of Keras, which sits on top of TensorFlow, CNTK, or Theano (though Theano is no longer being developed). \n", "\n", "ConX:\n", "\n", "* has an easy to use interface for creating connections between layers of a neural network\n", "* adds additional functionality for manipulating neural networks\n", "* supports visualizations and analysis for training and using neural networks\n", "* has everything you need; doesn't require knowledge of complicated numerical or plotting libraries\n", "* integrates with lower-level (Keras) if you wish\n", "\n", "But rather than attempting to explain each of these points, let's demonstrate them. There are 8 steps needed to construct and use a ConX network:\n", "\n", "1. import conx\n", "2. create the network\n", "3. add desired layers\n", "4. connect the layers\n", "5. compile the network, with a loss function and optimizer method\n", "6. create a dataset\n", "7. train the network\n", "8. test/analyze the network\n", "\n", "This demonstration is being run in a Jupyter Notebook. ConX doesn't require running in the notebook, but if you do, you will be able to use the visualizations and dashboard." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A Simple Network\n", "\n", "As a demonstration, let's build a simple network for learning the XOR (exclusive or). XOR is defined as:\n", "\n", "Input 1 | Input 2 | XOR\n", "--------|---------|-------\n", " 0 | 0 | 0\n", " 0 | 1 | 1\n", " 1 | 0 | 1\n", " 1 | 1 | 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 1: import conx\n", "\n", "We will need the Network, and Layer classes from the conx module:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n", "ConX, version 3.7.4\n" ] } ], "source": [ "import conx as cx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2: create the network\n", "\n", "Every network needs a name:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "net = cx.Network(\"XOR Network\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3: add the needed layers\n", "\n", "Every layer needs a name and a size. We add each of the layers of our network. The first layer will be an \"input\" layer (named arbitrarily \"input\"). We only need to specify the size. For our XOR problem, there are two inputs:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'input'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.add(cx.Layer(\"input\", 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the next layers, we will also use the default layer type for hidden and output layers. However, we also need to specify the function to apply to the \"net inputs\" to the layer, after the matrix multiplications. We have a few choices for which activation functions to use:\n", "\n", "* 'relu'\n", "* 'sigmoid'\n", "* 'linear'\n", "* 'softmax'\n", "* 'tanh'\n", "* 'elu'\n", "* 'selu'\n", "* 'softplus'\n", "* 'softsign'\n", "* 'hard_sigmoid'\n", "\n", "You can try any of these, though the sigmoid function generally works best for this problem. Feel free to experiment with other options." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'output'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.add(cx.Layer(\"hidden\", 3, activation=\"sigmoid\"))\n", "net.add(cx.Layer(\"output\", 1, activation=\"sigmoid\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 4: connect the layers\n", "\n", "We connect up the layers as needed. This is a simple 3-layer network:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "net.connect(\"input\", \"hidden\")\n", "net.connect(\"hidden\", \"output\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note**:\n", "\n", "We use the term `layer` here because each of these items composes the layer itself. In general though, a layer can be composed of many of these items. In that case, we call such a layer a `bank`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5: compile the network\n", "\n", "Before we can do this step, we need to do two things:\n", "\n", "1. tell the network how to compute the error between the targets and the actual outputs\n", "2. tell the network how to adjust the weights when learning\n", "\n", "#### Error (or loss)\n", "\n", "The first option is called the `error` (or `loss`). There are many choices for the error function, and we'll dive into each later. For now, we'll just briefly mention them:\n", "\n", "* \"mse\" - mean square error\n", "* \"mae\" - mean absolute error\n", "* \"mape\" - mean absolute percentage error\n", "* \"msle\" - mean squared logarithmic error\n", "* \"kld\" - kullback leibler divergence\n", "* \"cosine\" - cosine proximity\n", "\n", "#### Optimizer\n", "\n", "The second option is called \"optimizer\". Again, there are many choices, but we just briefly name them here:\n", "\n", "* \"sgd\" - Stochastic gradient descent optimizer\n", "* \"rmsprop\" - RMS Prop optimizer\n", "* \"adagrad\" - ADA gradient optimizer\n", "* \"adadelta\" - ADA delta optimizer\n", "* \"adam\" - [Adam optimizer](http://arxiv.org/abs/1412.6980v8)\n", "* \"adamax\" - Adamax optimizer from Adam\n", "* \"nadam\" - Nesterov Adam optimizer\n", "* \"tfoptimizer\" - a native TensorFlow optimizer\n", "\n", "For now, we'll just pick \"mse\" for the error function, and \"adam\" for the optimizer. \n", "\n", "And we compile the network:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "net.compile(error=\"mse\", optimizer=\"sgd\", lr=0.1, momentum=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Option: inspect the initial weights\n", "\n", "Networks in ConX are initialized with small random weights in the range -1..1. Each unit in a layer is also give a bias, which is initialized to 0. A bias is trained, just as the weights, and is added in when calculating a unit's incoming activation. Once trained, the bias provides each unit with a default activation value in the absence of other inputs.\n", "\n", "You can inspect the weights coming into a layer, as shown below." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[[-0.767471432685852, 0.10572397708892822, -0.7625459432601929],\n", " [-0.6755458116531372, 0.20516455173492432, 0.43674349784851074]],\n", " [0.0, 0.0, 0.0]]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.get_weights(\"hidden\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[[0.471946120262146], [0.9158862829208374], [1.1649702787399292]], [0.0]]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.get_weights(\"output\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Option: visualize the network\n", "\n", "At this point in the steps, you can see a visual representation of the network by simply asking for a picture:" ] }, { "cell_type": "code", "execution_count": 9, "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 = (3,)\n", " Keras class = Dense\n", " activation = sigmoidhiddenLayer: input (input)\n", " output range: (-Infinity, +Infinity)\n", " shape = (2,)\n", " Keras class = InputinputXOR Network" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.picture()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is useful to see the layers and connections." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Propagating the network places an array on the input layer, and sends the values through the network. We can try any input vector:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.8616290092468262]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate([-2, 2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we would like to see the activations on all of the units in the network, we can take a picture with the same input vector. You should show some colored squares in the layers representing the activation levels at each unit:" ] }, { "cell_type": "code", "execution_count": 11, "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 = (3,)\n", " Keras class = Dense\n", " activation = sigmoidhiddenLayer: input (input)\n", " output range: (-Infinity, +Infinity)\n", " shape = (2,)\n", " Keras class = InputinputXOR Network" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.picture([-2, 2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In these visualizations, the color gives an indication of its relative activation value of each neuron. \n", "\n", "For input layers, the default is to give a gray scale value representing the possible ranges, black meaning negative and white meaning more positive. \n", "\n", "For non-input layers, the more red a unit is, the smaller its value, and the more blue, the larger its value. Values close to zero will appear whiter.\n", "\n", "Notice that if you propagate this untrained network with zeros, then the hidden layer activations are all white. This means that there is no activation at any node in the network. This is because the bias units are initialized at zero." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.7818366289138794]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate([0,0])" ] }, { "cell_type": "code", "execution_count": 13, "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 = (3,)\n", " Keras class = Dense\n", " activation = sigmoidhiddenLayer: input (input)\n", " output range: (0, 0)\n", " shape = (2,)\n", " Keras class = InputinputXOR Network" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.picture([0,0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6: setup the training data\n", "\n", "For this little experiment, we want to train the network on our table from above. To do that, we add the inputs and the targets to the dataset, one at a time:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "net.dataset.append([0, 0], [0])\n", "net.dataset.append([0, 1], [1])\n", "net.dataset.append([1, 0], [1])\n", "net.dataset.append([1, 1], [0])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**Dataset**: Dataset for XOR Network\n", "\n", "**Information**:\n", " * name : None\n", " * length : 4\n", "\n", "**Input Summary**:\n", " * shape : (2,)\n", " * range : (0.0, 1.0)\n", "\n", "**Target Summary**:\n", " * shape : (1,)\n", " * range : (0.0, 1.0)\n", "\n" ], "text/plain": [ "**Dataset**: Dataset for XOR Network\n", "\n", "**Information**:\n", " * name : None\n", " * length : 4\n", "\n", "**Input Summary**:\n", " * shape : (2,)\n", " * range : (0.0, 1.0)\n", "\n", "**Target Summary**:\n", " * shape : (1,)\n", " * range : (0.0, 1.0)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net.dataset.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 7: train the network" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "net.reset()" ] }, { "cell_type": "code", "execution_count": 17, "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", " 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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", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "========================================================\n", " | Training | Training \n", "Epochs | Error | Accuracy \n", "------ | --------- | --------- \n", "# 4531 | 0.02997 | 1.00000 \n" ] } ], "source": [ "net.train(epochs=5000, accuracy=1.0, tolerance=0.2, report_rate=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhaps the network learned none, some, or all of the patterns. You can reset the network, and try again, by re-running the above cell. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 8: test the network" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "========================================================\n", "Testing validation dataset with tolerance 0.2...\n", "# | inputs | targets | outputs | result\n", "---------------------------------------\n", "0 | [[0.00, 0.00]] | [[0.00]] | [0.13] | correct\n", "1 | [[0.00, 1.00]] | [[1.00]] | [0.82] | correct\n", "2 | [[1.00, 0.00]] | [[1.00]] | [0.82] | correct\n", "3 | [[1.00, 1.00]] | [[0.00]] | [0.20] | correct\n", "Total count: 4\n", " correct: 4\n", " incorrect: 0\n", "Total percentage correct: 1.0\n" ] } ], "source": [ "net.evaluate(show=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The dashboard\n", "\n", "The dashboard allows you to interact, test, and generally work with your network via a GUI." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4c27b1ae838f40bcbf0848109f4163f9", "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": [ "ConX has a number of methods for visualizing images. In this example below, we get each picture of the network as an \"image\" and give the list of images to `cx.view`. " ] }, { "cell_type": "code", "execution_count": 20, "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 = (3,)\n", " Keras class = Dense\n", " activation = sigmoidhiddenLayer: input (input)\n", " output range: (0.0, 1.0)\n", " shape = (2,)\n", " Keras class = InputinputXOR Network" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "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 = (3,)\n", " Keras class = Dense\n", " activation = sigmoidhiddenLayer: input (input)\n", " output range: (0.0, 1.0)\n", " shape = (2,)\n", " Keras class = InputinputXOR Network" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "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 = (3,)\n", " Keras class = Dense\n", " activation = sigmoidhiddenLayer: input (input)\n", " output range: (0.0, 1.0)\n", " shape = (2,)\n", " Keras class = InputinputXOR Network" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "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 = (3,)\n", " Keras class = Dense\n", " activation = sigmoidhiddenLayer: input (input)\n", " output range: (0.0, 1.0)\n", " shape = (2,)\n", " Keras class = InputinputXOR Network" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(4):\n", " display(net.picture(net.dataset.inputs[i], scale=0.15))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ConX options" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Propagation functions\n", "\n", "There are five ways to propagate activations through the network:\n", "\n", "* Network.propagate(`inputs`) - propagate these inputs through the network\n", "* Network.propagate_to(`inputs`) - propagate these inputs to this bank (returns the output at that layer)\n", "* Network.propagate_from(`bank-name`, `activations`) - propagate the activations from `bank-name` to outputs\n", "* Network.propagate_to_image(`bank-name`, `activations`, scale=SCALE) - returns an image of the layer activations\n", "* Network.propagate_to_features(`bank-name`, `activations`, scale=SCALE) - gets a matrix of images for each feature (channel) at the layer\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.0340755395591259]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate_from(\"hidden\", [0, 1, 0])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.2813264727592468, 0.031041061505675316, 0.1953100860118866]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate_to(\"hidden\", [0.5, 0.5])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.36196693778038025, 0.26838693022727966, 0.06002996116876602]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate_to(\"hidden\", [0.1, 0.4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is also a propagate_to_image() that takes a bank name, and inputs." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.propagate_to_image(\"hidden\", [0.1, 0.4]).resize((500, 100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting options\n", "\n", "You can re-plot the plots from the entire training history with:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net.plot_results()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can plot the following values from the training history:\n", "\n", "* \"loss\" - error measure (eg, \"mse\", mean square error)\n", "* \"acc\" - the accuracy of the training set\n", "\n", "You can plot any subset of the above on the same plot:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net.plot([\"acc\", \"loss\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also see the activations at a particular unit, given a range of input values for two input units. Since this network has only two inputs, and one output, we can see the entire input and output ranges:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(net[\"hidden\"].size):\n", " net.plot_activation_map(from_layer=\"input\", from_units=(0,1),\n", " to_layer=\"hidden\", to_unit=i)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net.plot_activation_map(from_layer=\"input\", from_units=(0,1),\n", " to_layer=\"output\", to_unit=0)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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