add models

10 files changed, 62 insertions, 0 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/favicon.ico b/models/tensorspeare/favicon.ico
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+++ b/models/tensorspeare/favicon.ico
diff --git a/models/tensorspeare/index.html b/models/tensorspeare/index.html
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+++ b/models/tensorspeare/index.html
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+---
+layout: default
+title: Models
+---
+
+<script src="/assets/js/tf.min.js"></script>
+<script src="tensorspeare.js"></script>
+
+<div style="display: flex; flex-direction: column; justify-content: center; align-items: center; width: 100vw; max-height: 100vh; gap: 10px; padding-top: 10px">
+	<div>
+  	<label for="numletters" class="block mb-2 text-sm font-medium text-white">Number Of Letters To Generate</label>
+  	<input type="number" id="numletters" style="font-size: 1.125rem; border-radius: 0.5rem; display: block; width: 100%; padding: 0.625rem; background-color: #2f3238; border-color: #444444; placeholder-color: #444444; color: #ffffff; value='1000';" value="1000">
+  </div>
+	<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>
+	<textarea readonly id="generated_text" rows="10" style="width: 55%; min-width: 400px; background-color: #1f1d29; resize:none; color: #FFFFFF; outline: none"></textarea>
+</div>
diff --git a/models/tensorspeare/jsmodel/group1-shard1of4.bin b/models/tensorspeare/jsmodel/group1-shard1of4.bin
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+++ b/models/tensorspeare/jsmodel/group1-shard1of4.bin
diff --git a/models/tensorspeare/jsmodel/group1-shard2of4.bin b/models/tensorspeare/jsmodel/group1-shard2of4.bin
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+++ b/models/tensorspeare/jsmodel/group1-shard2of4.bin
diff --git a/models/tensorspeare/jsmodel/group1-shard3of4.bin b/models/tensorspeare/jsmodel/group1-shard3of4.bin
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index 0000000..8613500
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+++ b/models/tensorspeare/jsmodel/group1-shard3of4.bin
diff --git a/models/tensorspeare/jsmodel/group1-shard4of4.bin b/models/tensorspeare/jsmodel/group1-shard4of4.bin
new file mode 100644
index 0000000..e953467
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+++ b/models/tensorspeare/jsmodel/group1-shard4of4.bin
diff --git a/models/tensorspeare/jsmodel/model.json b/models/tensorspeare/jsmodel/model.json
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+++ b/models/tensorspeare/jsmodel/model.json
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+{"format": "layers-model", "generatedBy": "keras v2.4.0", "convertedBy": "TensorFlow.js Converter v4.2.0", "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"}]}]}
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diff --git a/models/tensorspeare/model.h5 b/models/tensorspeare/model.h5
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index 0000000..8bbd76d
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+++ b/models/tensorspeare/model.h5
diff --git a/models/tensorspeare/tensorspeare.js b/models/tensorspeare/tensorspeare.js
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+++ b/models/tensorspeare/tensorspeare.js
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+async function generate_text(model, idx2char, char2idx, btn, 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 = [];
+
+  model.resetStates();
+  for (let i = 0; i < num_generate; i++) {
+		btn.textContent = `Generating ${Math.trunc(i/num_generate*100)}%`
+    let predictions = model.predict(input_eval);
+     predictions = predictions.squeeze();
+    predictions = predictions.div(temperature);
+    let predicted_id = await tf.multinomial(predictions, 1).dataSync()[0];
+		input_eval = tf.expandDims([predicted_id], 0);
+    text_generated.push(idx2char[predicted_id]);
+		await new Promise(resolve => setTimeout(resolve, 5));
+	}
+
+  generated_text.value = start_string + '\n' + text_generated.join('');
+}
+
+async function runModel(num_generate) {
+	btn = document.getElementById("mybutton")
+	btn.textContent = "Generating 0%"
+
+	generated_text = document.getElementById("generated_text")
+	char2idx = {'\n': 0, ' ': 1, '!': 2, '$': 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, btn, generated_text, num_generate=num_generate);
+	btn.textContent = "Generate"
+}