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speech-scoring
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saving models and logs

master
Malar Kannan 2017-10-24 11:49:47 +05:30
parent 03d49d83e7
commit 8be8fa2595
2 changed files with 20 additions and 10 deletions
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2
.gitignore vendored
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@ -140,4 +140,6 @@ Temporary Items
outputs/* outputs/*
inputs/mnist inputs/mnist
inputs/audio* inputs/audio*
logs/*
models/*
*.pkl *.pkl

28
Siamese.ipynb
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@ -175,7 +175,22 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 36252 samples, validate on 4028 samples\n"
]
}
],
"source": [ "source": [
"'''Train a Siamese MLP on pairs of digits from the MNIST dataset.\n", "'''Train a Siamese MLP on pairs of digits from the MNIST dataset.\n",
"\n", "\n",
@ -294,7 +309,7 @@
"\n", "\n",
"model = Model([input_a, input_b], distance)\n", "model = Model([input_a, input_b], distance)\n",
"\n", "\n",
"tb_cb = TensorBoard(log_dir='./siamese_logs', histogram_freq=1, batch_size=32,\n", "tb_cb = TensorBoard(log_dir='./logs/siamese_logs', histogram_freq=1, batch_size=32,\n",
" write_graph=True, write_grads=True, write_images=True,\n", " write_graph=True, write_grads=True, write_images=True,\n",
" embeddings_freq=0, embeddings_layer_names=None,\n", " embeddings_freq=0, embeddings_layer_names=None,\n",
" embeddings_metadata=None)\n", " embeddings_metadata=None)\n",
@ -308,7 +323,7 @@
" validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),\n", " validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),\n",
" callbacks=[tb_cb])\n", " callbacks=[tb_cb])\n",
"\n", "\n",
"model.save('./siamese_speech_model.h5')\n", "model.save('./models/siamese_speech_model.h5')\n",
"# compute final accuracy on training and test sets\n", "# compute final accuracy on training and test sets\n",
"y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])\n", "y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])\n",
"tr_acc = compute_accuracy(tr_y, y_pred)\n", "tr_acc = compute_accuracy(tr_y, y_pred)\n",
@ -318,13 +333,6 @@
"print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))\n", "print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))\n",
"print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))" "print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))"
] ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {