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Adds ml5.js v0.3.0 to release (#144)
* set ml5 reference to localhost * Unet example webcam (#120) * added uNet example * rm video from model load * updated UNET example to current UNET implementation * set frameRate to 5 * Tinkering with segmentation example (#121) I am not sure this is better. I do prefer calling `segment()` from `gotResult()` itself, but maybe creating a blank image to eliminate the `draw()` if statement isn't a good idea. * Sentiment example (#118) * added sentiment example * move to p5js * fixed unet sketch at segmentationImage (#122) * add cvae example (#117) * added new cvae example (#123) * handle html elements in p5 and cleaned up functions (#124) * simplify CVAE example (#125) * simplfiying the sentiment example (#126) * simplify unet example (#127) * Update localhost reference and script (#129) * added http://localhost:8080/ml5.js * added all examples with correct http://localhost:8080/ml5.js * changing example to dropdown (#130) * changing example to dropdown * comments * Adds DCGAN example (#138) * initial commit * adds example of dcgan using small face model * use named function for callback * rm console.log * updated className to label (#141) * Feature extractor update to specify number of labels (#142) * added additional label - show how to specify label nums * adds object as input to .classificaiton() * changed version ml5 reference to 0.3.0 * added p5 version 0.8.0
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‎javascript/FeatureExtractor_Image_Classification/index.html‎

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<title>Image Classification using Feature Extraction with MobileNet</title>
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<script src="https://unpkg.com/ml5@0.2.3/dist/ml5.min.js" type="text/javascript"></script>
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<script src="https://unpkg.com/ml5@0.3.0/dist/ml5.min.js" type="text/javascript"></script>
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<style></style>
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</head>

‎javascript/ImageClassification/index.html‎

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<title>Image Classification Example</title>
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<script src="https://unpkg.com/ml5@0.2.3/dist/ml5.min.js" type="text/javascript"></script>
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<script src="https://unpkg.com/ml5@0.3.0/dist/ml5.min.js" type="text/javascript"></script>
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</head>
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<body>
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<h1>Image classification using MobileNet</h1>
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<p>The MobileNet model labeled this as <span id="result">...</span> with a confidence of <span id="probability">...</span>.</p>
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<img src="images/bird.jpg" id="image" width="400" />
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<img src="images/bird.jpg" id="image" width="400" />
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<script src="sketch.js"></script>
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</body>
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‎javascript/ImageClassification_Video/index.html‎

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<title>Webcam Image Classification using MobileNet</title>
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<script src="https://unpkg.com/ml5@0.2.3/dist/ml5.min.js" type="text/javascript"></script>
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<script src="https://unpkg.com/ml5@0.3.0/dist/ml5.min.js" type="text/javascript"></script>
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‎javascript/PoseNet/index.html‎

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<meta charset="UTF-8">
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<title>PoseNet Example</title>
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<script src="https://unpkg.com/ml5@0.2.3/dist/ml5.min.js" type="text/javascript"></script>
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<script src="https://unpkg.com/ml5@0.3.0/dist/ml5.min.js" type="text/javascript"></script>
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‎javascript/Sentiment/index.html‎

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<html>
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<head>
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<title>ml5 - Sentiment</title>
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<script src="https://unpkg.com/ml5@0.3.0/dist/ml5.min.js" type="text/javascript"></script>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.8.0/p5.min.js"></script>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.8.0/addons/p5.dom.min.js"></script>
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<style></style>
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</head>
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<body>
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<h1>Sentiment Analysis Demo
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</h1>
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<h2>Using a pre-trained model from TensorFlow.js Layers trained on short movie review. <br />
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This model is trained to predict the sentiment of a short movie review (as a score between 0 and 1)
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</h2>
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<h3>
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The model is trained using movie reviews that have been truncated to a maximum of 200 words, only the 20000 most used
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words in the reviews are used.
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</h3>
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<p id='status'></p>
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<div>
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<div class="row">
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<p>
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<input type="text" style="width: 100%" placeholder="input sentence to analize" id="inputText" />
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</p>
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<p>
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<button id="submit" class="btn btn-primary">predict sentiment</button>
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</p>
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<p id="sentiment-res">Sentiment score:</p>
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</div>
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</div>
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<script src="sketch.js"></script>
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</body>
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</html>

‎javascript/StyleTransfer_Image/index.html‎

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<head>
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<meta charset="UTF-8">
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<title>Style Transfer Image Example with Promises</title>
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<script src="https://unpkg.com/ml5@0.2.3/dist/ml5.min.js" type="text/javascript"></script>
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<script src="https://unpkg.com/ml5@0.3.0/dist/ml5.min.js" type="text/javascript"></script>
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<style></style>
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<h1>Style Transfer Image Example</h1>
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<p id="statusMsg">Loading Models...</p>
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<p>Input Image:</p>
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<img src="img/patagonia.jpg" alt="input img" id="inputImg" />
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<img src="img/patagonia.jpg" alt="input img" id="inputImg" />
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<div id="styleA">
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<p>Style A: <a href="https://en.wikipedia.org/wiki/The_Great_Wave_off_Kanagawa">The Great Wave off Kanagawa, 1829 - Katsushika Hokusai</a></p>
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<img src="img/wave.jpg" alt="style one" />
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<img src="img/wave.jpg" alt="style one" />
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</div>
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<div id="styleB">
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<p>Style B: <a href="https://en.wikipedia.org/wiki/The_Great_Wave_off_Kanagawa">Udnie (Young American Girl, The Dance), 1913 - Francis Picabia</a></p>
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<img src="img/udnie.jpg" alt="style two" />
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<img src="img/udnie.jpg" alt="style two" />
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‎p5js/CVAE/index.html‎

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<html>
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<head>
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<meta charset="UTF-8" >
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<meta http-equiv="X-UA-Compatible" content="IE=edge" >
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<meta name="viewport" content="width=device-width, initial-scale=1" >
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<title>CVAE with quick_draw</title>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.8.0/p5.min.js"></script>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.8.0/addons/p5.dom.min.js"></script>
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<script src="https://unpkg.com/ml5@0.3.0/dist/ml5.min.js" type="text/javascript"></script>
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<script src="sketch.js"></script>
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</head>
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<body>
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<h1>Conditional Variational Autoencoder (CVAE) - trained on Google Quick Draw</h1>
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</body>
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</html>

‎p5js/CVAE/model/quick_draw/manifest.json‎

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{"model": "models/model.json", "labels": ["airplane", "alarm_clock", "ambulance", "angel", "ant", "anvil", "apple", "axe", "pickup_truck", "laptop", "leaf", "light_bulb", "lighter", "lightning", "lion", "lipstick", "lobster", "lollipop", "mailbox", "megaphone", "mermaid", "microphone", "microwave", "monkey", "moon", "mosquito", "motorbike", "mountain", "mouse", "mouth", "mug", "mushroom", "nail", "necklace", "ocean", "octopus", "onion", "oven", "owl", "panda", "pants", "paper_clip", "parachute", "ice_cream", "jacket", "jail", "kangaroo", "key", "keyboard", "knife"]}

‎p5js/CVAE/model/quick_draw/models/group1-shard1of1‎

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‎p5js/CVAE/model/quick_draw/models/model.json‎

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{"modelTopology": {"keras_version": "2.2.2", "backend": "tensorflow", "model_config": {"class_name": "Model", "config": {"name": "decoder", "layers": [{"name": "latent_input", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 16], "dtype": "float32", "sparse": false, "name": "latent_input"}, "inbound_nodes": []}, {"name": "label", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 51], "dtype": "float32", "sparse": false, "name": "label"}, "inbound_nodes": []}, {"name": "concatenate_2", "class_name": "Concatenate", "config": {"name": "concatenate_2", "trainable": true, "axis": -1}, "inbound_nodes": [[["latent_input", 0, 0, {}], ["label", 0, 0, {}]]]}, {"name": "dense_5", "class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "units": 784, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["concatenate_2", 0, 0, {}]]]}, {"name": "reshape_2", "class_name": "Reshape", "config": {"name": "reshape_2", "trainable": true, "target_shape": [7, 7, 16]}, "inbound_nodes": [[["dense_5", 0, 0, {}]]]}, {"name": "conv2d_transpose_1", "class_name": "Conv2DTranspose", "config": {"name": "conv2d_transpose_1", "trainable": true, "filters": 32, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null, "output_padding": null}, "inbound_nodes": [[["reshape_2", 0, 0, {}]]]}, {"name": "up_sampling2d_1", "class_name": "UpSampling2D", "config": {"name": "up_sampling2d_1", "trainable": true, "size": [2, 2], "data_format": "channels_last"}, "inbound_nodes": [[["conv2d_transpose_1", 0, 0, {}]]]}, {"name": "conv2d_transpose_2", "class_name": "Conv2DTranspose", "config": {"name": "conv2d_transpose_2", "trainable": true, "filters": 16, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null, "output_padding": null}, "inbound_nodes": [[["up_sampling2d_1", 0, 0, {}]]]}, {"name": "up_sampling2d_2", "class_name": "UpSampling2D", "config": {"name": "up_sampling2d_2", "trainable": true, "size": [2, 2], "data_format": "channels_last"}, "inbound_nodes": [[["conv2d_transpose_2", 0, 0, {}]]]}, {"name": "conv2d_transpose_3", "class_name": "Conv2DTranspose", "config": {"name": "conv2d_transpose_3", "trainable": true, "filters": 1, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null, "output_padding": null}, "inbound_nodes": [[["up_sampling2d_2", 0, 0, {}]]]}], "input_layers": [["latent_input", 0, 0], ["label", 0, 0]], "output_layers": [["conv2d_transpose_3", 0, 0]]}}}, "weightsManifest": [{"paths": ["group1-shard1of1"], "weights": [{"name": "conv2d_transpose_1/kernel", "shape": [3, 3, 32, 16], "dtype": "float32"}, {"name": "conv2d_transpose_1/bias", "shape": [32], "dtype": "float32"}, {"name": "conv2d_transpose_2/kernel", "shape": [3, 3, 16, 32], "dtype": "float32"}, {"name": "conv2d_transpose_2/bias", "shape": [16], "dtype": "float32"}, {"name": "conv2d_transpose_3/kernel", "shape": [3, 3, 1, 16], "dtype": "float32"}, {"name": "conv2d_transpose_3/bias", "shape": [1], "dtype": "float32"}, {"name": "dense_5/kernel", "shape": [67, 784], "dtype": "float32"}, {"name": "dense_5/bias", "shape": [784], "dtype": "float32"}]}]}

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