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YasuThompson/translator_wp_6.ipynb Secret

Created March 17, 2021 14:48
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translator_wp_6.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"en_padded = tf.keras.preprocessing.sequence.pad_sequences(en_encoded, padding='post')\n",
"de_padded = tf.keras.preprocessing.sequence.pad_sequences(de_encoded, padding='post')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def create_padding_mask(seq):\n",
" seq = tf.cast(tf.math.equal(seq, 0), tf.float32)\n",
"\n",
" # add extra dimensions to add the padding\n",
" # to the attention logits.\n",
" return seq[:, tf.newaxis, tf.newaxis, :] # (batch_size, 1, 1, seq_len)\n",
"\n",
"def create_look_ahead_mask(size):\n",
" mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)\n",
" return mask # (seq_len, seq_len)\n",
"\n",
"def create_masks(inp, tar):\n",
" # Encoder padding mask\n",
" enc_padding_mask = create_padding_mask(inp)\n",
"\n",
" # Used in the 2nd attention block in the decoder.\n",
" # This padding mask is used to mask the encoder outputs.\n",
" dec_padding_mask = create_padding_mask(inp)\n",
"\n",
" # Used in the 1st attention block in the decoder.\n",
" # It is used to pad and mask future tokens in the input received by \n",
" # the decoder.\n",
" look_ahead_mask = create_look_ahead_mask(tf.shape(tar)[1])\n",
" dec_target_padding_mask = create_padding_mask(tar)\n",
" combined_mask = tf.maximum(dec_target_padding_mask, look_ahead_mask)\n",
"\n",
" return enc_padding_mask, combined_mask, dec_padding_mask\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[10000, 822, 9935, 10001, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0],\n",
" [10000, 386, 8371, 2234, 5359, 1697, 1860, 26, 3285,\n",
" 754, 2161, 1601, 9934, 777, 662, 505, 86, 2383,\n",
" 42, 3690, 120, 1294, 1220, 309, 5481, 80, 3581,\n",
" 42, 86, 1515, 34, 2254, 9940, 9920, 359, 37,\n",
" 9935, 10001, 0, 0, 0]], dtype=int32)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# When you make a sample batch with the first and the last sentence, the batch in English look slike below. \n",
"en_sample_batch, de_sample_batch = np.stack((en_padded[0], en_padded[-1])), np.stack((de_padded[0], de_padded[-1]))\n",
"en_sample_batch"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# You can make all the masks needed for the batch with the function create_masks() funciton. \n",
"enc_padding_mask, combined_mask, dec_padding_mask = create_masks(en_sample_batch, de_sample_batch)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(TensorShape([2, 1, 1, 41]), TensorShape([2, 1, 1, 41]))"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Importantly enc_padding_mask and dec_padding_mask have the same shape. \n",
"enc_padding_mask.shape, dec_padding_mask.shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(2, 1, 1, 41), dtype=float32, numpy=\n",
"array([[[[0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]],\n",
"\n",
"\n",
" [[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.]]]], dtype=float32)>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The encoder padding mask and the decoder padding mask are the same, \n",
"# and they are quite simple. You can see \n",
"enc_padding_mask"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(2, 1, 1, 41), dtype=float32, numpy=\n",
"array([[[[0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]],\n",
"\n",
"\n",
" [[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.]]]], dtype=float32)>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dec_padding_mask"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Let's take a sample tensor with random elements as an example. \n",
"# You just add -1e9 to the elements where quivalent elemets of masks are 1. \n",
"# After applying a softmax functino you can zero pad positions of multi-head attention maps \n",
"# where the input sentences are also zero padded. \n",
"sample_tensor = np.random.rand(2, 8, 41, 41)\n",
"sample_tensor = sample_tensor+ (enc_padding_mask * -1e9)\n",
"attention_map = tf.nn.softmax(sample_tensor, axis=-1)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\n",
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]
}
],
"source": [
"# When you print out averything, the attention map of the encoder part looks like this. \n",
"tf.print(attention_map, summarize=-1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [
"s_qNSzzyaCbD"
],
"name": "transformer.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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