9 files changed, 0 insertions, 70 deletions
diff --git a/models/index.html b/models/index.html
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----
-layout: default
-title: Models
----
-<div class="flex flex-row flex-wrap" onclick="window.location='/models/tensorspeare'">
-    <div class="blog-block flex-col break-words transform hover:bg-gray-900
-                justify-start max-w-sm" style="cursor: pointer;">
-        <a class="text-xl text-white mt-2 border-b pb-1 hover:underline">Tensorspeare</a>
-        <a class="text-lg text-gray-400 pt-2">A TensorFlow model which generates text that sounds like it was written by Shakespeare </a>
-    </div>
-</div>
diff --git a/models/tensorspeare/index.md b/models/tensorspeare/index.md
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----
-title: TensorSpeare
-layout: model
----
-### TensorSpeare
-
-A TensorFlow model which generates text that tries to imitate Shakespeare.
-
-*Trained using Google's Shakespeare dataset*
-
-### Layers
-
-1. Embedding
-
-2. Gated Recurrent Unit
-
-3. Dense
-
-### Try It
-
-<script src="/assets/js/tf.min.js"></script>
-<script src="tensorspeare.js"></script>
-
-<div class="flex flex-col justify-left items-left">
-<textarea readonly id="generated_text" rows="10" style="min-width: 400px; background-color: #1f1d29; resize:none; color: #FFFFFF; outline: none"></textarea>
-&nbsp;
-<button id="mybutton" class="button" style="color: #fff; background-color: #6a6980; border: none; padding: 15px 32px; text-align: center; text-decoration: none; display: inline-block; font-size: 16px;" onclick="runModel()">Generate</button>
-</div>
diff --git a/models/tensorspeare/jsmodel/group1-shard1of4.bin b/models/tensorspeare/jsmodel/group1-shard1of4.bin
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diff --git a/models/tensorspeare/jsmodel/group1-shard2of4.bin b/models/tensorspeare/jsmodel/group1-shard2of4.bin
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diff --git a/models/tensorspeare/jsmodel/group1-shard3of4.bin b/models/tensorspeare/jsmodel/group1-shard3of4.bin
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diff --git a/models/tensorspeare/jsmodel/group1-shard4of4.bin b/models/tensorspeare/jsmodel/group1-shard4of4.bin
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--- a/models/tensorspeare/jsmodel/group1-shard4of4.bin
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diff --git a/models/tensorspeare/jsmodel/model.json b/models/tensorspeare/jsmodel/model.json
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--- a/models/tensorspeare/jsmodel/model.json
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@@ -1 +0,0 @@
-{"format": "layers-model", "generatedBy": "keras v2.4.0", "convertedBy": "TensorFlow.js Converter v4.4.0", "modelTopology": {"keras_version": "2.4.0", "backend": "tensorflow", "model_config": {"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [1, null], "dtype": "float32", "sparse": false, "ragged": false, "name": "embedding_1_input"}}, {"class_name": "Embedding", "config": {"name": "embedding_1", "trainable": true, "batch_input_shape": [1, null], "dtype": "float32", "input_dim": 65, "output_dim": 256, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -0.05, "maxval": 0.05, "seed": null}}, "embeddings_regularizer": null, "activity_regularizer": null, "embeddings_constraint": null, "mask_zero": false, "input_length": null}}, {"class_name": "GRU", "config": {"name": "gru_1", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": true, "unroll": false, "time_major": false, "units": 1024, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2, "reset_after": false}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 65, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}}}, "weightsManifest": [{"paths": ["group1-shard1of4.bin", "group1-shard2of4.bin", "group1-shard3of4.bin", "group1-shard4of4.bin"], "weights": [{"name": "dense_1/kernel", "shape": [1024, 65], "dtype": "float32"}, {"name": "dense_1/bias", "shape": [65], "dtype": "float32"}, {"name": "embedding_1/embeddings", "shape": [65, 256], "dtype": "float32"}, {"name": "gru_1/gru_cell_1/kernel", "shape": [256, 3072], "dtype": "float32"}, {"name": "gru_1/gru_cell_1/recurrent_kernel", "shape": [1024, 3072], "dtype": "float32"}, {"name": "gru_1/gru_cell_1/bias", "shape": [3072], "dtype": "float32"}]}]}
-\ No newline at end of file
diff --git a/models/tensorspeare/model.h5 b/models/tensorspeare/model.h5
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--- a/models/tensorspeare/model.h5
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diff --git a/models/tensorspeare/tensorspeare.js b/models/tensorspeare/tensorspeare.js
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--- a/models/tensorspeare/tensorspeare.js
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-async function generate_text(model, idx2char, char2idx, generated_text, start_string = 'ROMEO:', num_generate = 1000, temperature = 0.1) {
-  let input_eval = start_string.split('').map(s => char2idx[s]);
-  input_eval = tf.tensor1d(input_eval);
-  input_eval = tf.reshape(input_eval, [1, input_eval.size]);
-
-  let text_generated = [];
-
-	generated_text.value = start_string + "\n"
-
-  model.resetStates();
-  for (let i = 0; i < num_generate; i++) {
-    let predictions = model.predict(input_eval);
-    predictions = predictions.squeeze();
-    predictions = predictions.div(temperature);
-    let predicted_id = await tf.multinomial(predictions, 1).dataSync()[0];
-		input_eval = tf.expandDims([predicted_id], 0);
-		generated_text.value = generated_text.value + idx2char[predicted_id]
-		await new Promise(resolve => setTimeout(resolve, 5));
-	}
-}
-
-async function runModel(num_generate) {
-
-	generated_text = document.getElementById("generated_text")
-	char2idx = {'\n': 0, ' ': 1, '!': 2, '$': 3, '&': 4, "'": 5, ',': 6, '-': 7, '.': 8, '3': 9, ':': 10, ';': 11, '?': 12, 'A': 13, 'B': 14, 'C': 15, 'D': 16, 'E': 17, 'F': 18, 'G': 19, 'H': 20, 'I': 21, 'J': 22, 'K': 23, 'L': 24, 'M': 25, 'N': 26, 'O': 27, 'P': 28, 'Q': 29, 'R': 30, 'S': 31, 'T': 32, 'U': 33, 'V': 34, 'W': 35, 'X': 36, 'Y': 37, 'Z': 38, 'a': 39, 'b': 40, 'c': 41, 'd': 42, 'e': 43, 'f': 44, 'g': 45, 'h': 46, 'i': 47, 'j': 48, 'k': 49, 'l': 50, 'm': 51, 'n': 52, 'o': 53, 'p': 54, 'q': 55, 'r': 56, 's': 57, 't': 58, 'u': 59, 'v': 60, 'w': 61, 'x': 62, 'y': 63, 'z': 64}
-
-	idx2char = ['\n', ' ', '!', '$', '&', "'", ',', '-', '.', '3', ':', ';', '?', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
-	const model = await tf.loadLayersModel('/models/tensorspeare/jsmodel/model.json');
-	generate_text(model, idx2char, char2idx, generated_text, num_generate=num_generate);
-}