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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"length of the longest sentence: 10\n",
"no_of_sentences: 127946\n"
]
}
],
"source": [
"import os\n",
"import random\n",
"\n",
"#os.environ['CUDA_VISIBLE_DEVICES'] = '-1' #disble gpu\n",
"\n",
"def get_text_data():\n",
" sentences=[]\n",
" file_name=\"cleaned_assamese_text.txt\"\n",
" file=open(file_name,'r')\n",
" file_sentences=file.read().split(',')\n",
" sentences+=file_sentences\n",
" file.close()\n",
" sentences=list(filter(None,sentences))\n",
" return sentences\n",
"\n",
"sentences=get_text_data()\n",
"random.shuffle(sentences)\n",
"no_of_sentences=len(sentences)\n",
"text_train=sentences[:int(0.7*no_of_sentences)]\n",
"text_test=sentences[int(0.7*no_of_sentences):int(0.85*no_of_sentences)]\n",
"text_valid=sentences[int(0.85*no_of_sentences):]\n",
"#maxlen = len(max(sentences))\n",
"maxlen=10\n",
"print(\"length of the longest sentence: \",maxlen)\n",
"print(\"no_of_sentences: \",no_of_sentences)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-02-28 23:36:00.068548: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2023-02-28 23:36:01.115879: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-12.0/lib64:/usr/local/cuda-11.7/lib64::/home/yuvrajtalukdar/miniconda3/envs/miniproject/lib/\n",
"2023-02-28 23:36:01.116220: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-12.0/lib64:/usr/local/cuda-11.7/lib64::/home/yuvrajtalukdar/miniconda3/envs/miniproject/lib/\n",
"2023-02-28 23:36:01.116238: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
"2023-02-28 23:36:02.603014: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-02-28 23:36:02.736211: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-02-28 23:36:02.736438: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-02-28 23:36:02.736847: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2023-02-28 23:36:02.737278: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-02-28 23:36:02.737453: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-02-28 23:36:02.737574: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-02-28 23:36:03.410798: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-02-28 23:36:03.410969: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-02-28 23:36:03.411092: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-02-28 23:36:03.411205: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2107 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3050 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"303475\n"
]
},
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(1, 11), dtype=int64, numpy=array([[ 17, 3078, 2246, 87, 31, 0, 0, 0, 0, 0, 0]])>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from tensorflow.keras.layers import TextVectorization\n",
"import tensorflow as tf\n",
"\n",
"def custom_standardization(input_string):\n",
" sentence = tf.strings.lower(input_string)\n",
" #sentence = tf.strings.regex_replace(sentence, \"\\n\", \" \")\n",
" return sentence\n",
"\n",
"vectorize_layer = TextVectorization(\n",
" standardize = custom_standardization,\n",
" output_mode=\"int\",\n",
" output_sequence_length=maxlen + 1,\n",
")\n",
"\n",
"vectorize_layer.adapt(sentences)\n",
"vocab = vectorize_layer.get_vocabulary()\n",
"\n",
"vocab_size = len(vocab)\n",
"print(vocab_size) # 49703\n",
"vectorize_layer(['এক অনন্য মাত্ৰা প্ৰদান কৰাৰ'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"index_lookup = dict(zip(range(len(vocab)), vocab))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"batch_size = 10 #64\n",
"\n",
"train_dataset = tf.data.Dataset.from_tensor_slices(text_train)\n",
"train_dataset = train_dataset.shuffle(buffer_size=256)\n",
"train_dataset = train_dataset.batch(batch_size)\n",
"\n",
"test_dataset = tf.data.Dataset.from_tensor_slices(text_test)\n",
"test_dataset = test_dataset.shuffle(buffer_size=256)\n",
"test_dataset = test_dataset.batch(batch_size)\n",
"\n",
"valid_dataset = tf.data.Dataset.from_tensor_slices(text_valid)\n",
"valid_dataset = valid_dataset.shuffle(buffer_size=256)\n",
"valid_dataset = valid_dataset.batch(batch_size)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def preprocess_text(text):\n",
" text = tf.expand_dims(text, -1)\n",
" tokenized_sentences = vectorize_layer(text)\n",
" x = tokenized_sentences[:, :-1]\n",
" y = tokenized_sentences[:, 1:]\n",
" return x, y\n",
"\n",
"\n",
"train_dataset = train_dataset.map(preprocess_text)\n",
"train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)\n",
"\n",
"test_dataset = test_dataset.map(preprocess_text)\n",
"test_dataset = test_dataset.prefetch(tf.data.AUTOTUNE)\n",
"\n",
"valid_dataset = valid_dataset.map(preprocess_text)\n",
"valid_dataset = valid_dataset.prefetch(tf.data.AUTOTUNE)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(<tf.Tensor: shape=(10, 10), dtype=int64, numpy=\n",
"array([[ 10738, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0],\n",
" [ 5212, 24846, 504, 51, 71, 8517, 6751, 4828,\n",
" 681, 0],\n",
" [ 61, 108, 7418, 252, 2823, 2674, 134, 487,\n",
" 0, 0],\n",
" [289690, 2, 112988, 1054, 5367, 31142, 22, 3240,\n",
" 1115, 2376],\n",
" [ 393, 2, 352, 125, 6995, 6019, 41625, 12,\n",
" 1799, 551],\n",
" [ 265, 4642, 22, 1696, 89473, 126, 3, 5,\n",
" 410, 3375],\n",
" [ 8187, 18122, 278, 34, 579, 579, 43, 1119,\n",
" 710, 395],\n",
" [ 61, 16, 5291, 150, 1166, 2, 4796, 50192,\n",
" 5668, 2324],\n",
" [ 52, 954, 239, 595, 5401, 1006, 2, 3253,\n",
" 3812, 21],\n",
" [ 17071, 2, 15782, 5901, 15075, 783, 22, 40,\n",
" 40782, 34480]])>, <tf.Tensor: shape=(10, 10), dtype=int64, numpy=\n",
"array([[ 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0],\n",
" [ 24846, 504, 51, 71, 8517, 6751, 4828, 681,\n",
" 0, 0],\n",
" [ 108, 7418, 252, 2823, 2674, 134, 487, 0,\n",
" 0, 0],\n",
" [ 2, 112988, 1054, 5367, 31142, 22, 3240, 1115,\n",
" 2376, 2483],\n",
" [ 2, 352, 125, 6995, 6019, 41625, 12, 1799,\n",
" 551, 20],\n",
" [ 4642, 22, 1696, 89473, 126, 3, 5, 410,\n",
" 3375, 4436],\n",
" [ 18122, 278, 34, 579, 579, 43, 1119, 710,\n",
" 395, 710],\n",
" [ 16, 5291, 150, 1166, 2, 4796, 50192, 5668,\n",
" 2324, 239],\n",
" [ 954, 239, 595, 5401, 1006, 2, 3253, 3812,\n",
" 21, 245],\n",
" [ 2, 15782, 5901, 15075, 783, 22, 40, 40782,\n",
" 34480, 0]])>)\n"
]
}
],
"source": [
"for entry in train_dataset.take(1):\n",
" print(entry)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import keras_nlp\n",
"from tensorflow import keras\n",
"\n",
"embed_dim = 128\n",
"num_heads = 4\n",
"\n",
"def create_model2(no_of_decoder=1):\n",
" inputs = keras.layers.Input(shape=(maxlen,), dtype=tf.int32)\n",
" x = keras_nlp.layers.TokenAndPositionEmbedding(vocab_size, maxlen, embed_dim)(inputs)\n",
" for i in range(4):\n",
" x = keras_nlp.layers.TransformerDecoder(intermediate_dim=embed_dim*2, num_heads=num_heads,dropout=0.5)(x)\n",
" do = keras.layers.Dropout(0.4)(x)\n",
" outputs = keras.layers.Dense(vocab_size, activation='softmax')(do)\n",
" \n",
" model = keras.Model(inputs=inputs, outputs=outputs)\n",
" model.compile(\n",
" optimizer=\"adam\", \n",
" loss='sparse_categorical_crossentropy',\n",
" metrics=[keras_nlp.metrics.Perplexity(), 'accuracy']\n",
" )\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"class TextSampler(keras.callbacks.Callback):\n",
" def __init__(self, start_prompt, max_tokens):\n",
" self.start_prompt = start_prompt\n",
" self.max_tokens = max_tokens\n",
" \n",
" # Helper method to choose a word from the top K probable words with respect to their probabilities\n",
" # in a sequence\n",
" def sample_token(self, logits):\n",
" logits, indices = tf.math.top_k(logits, k=5, sorted=True)\n",
" indices = np.asarray(indices).astype(\"int32\")\n",
" preds = keras.activations.softmax(tf.expand_dims(logits, 0))[0]\n",
" preds = np.asarray(preds).astype(\"float32\")\n",
" return np.random.choice(indices, p=preds)\n",
"\n",
" def on_epoch_end(self, epoch, logs=None):\n",
" decoded_sample = self.start_prompt\n",
" \n",
" for i in range(self.max_tokens-1):\n",
" tokenized_prompt = vectorize_layer([decoded_sample])[:, :-1]\n",
" predictions = self.model.predict([tokenized_prompt], verbose=0)\n",
" # To find the index of the next word in the prediction array.\n",
" # The tokenized prompt is already shorter than the original decoded sample\n",
" # by one, len(decoded_sample.split()) is two words ahead - so we remove 1 to get\n",
" # the next word in the sequence\n",
" sample_index = len(decoded_sample.strip().split())-1\n",
" \n",
" sampled_token = self.sample_token(predictions[0][sample_index])\n",
" sampled_token = index_lookup[sampled_token]\n",
" decoded_sample += \" \" + sampled_token\n",
" \n",
" print(f\"\\nSample text:\\n{decoded_sample}...\\n\")\n",
"\n",
"# First 5 words of a random sentence to be used as a seed\n",
"random_sentence = ' '.join(random.choice(text_valid).replace('\\n', ' ').split(' ')[:4])\n",
"sampler = TextSampler(random_sentence, 30)\n",
"reducelr = keras.callbacks.ReduceLROnPlateau(patience=10, monitor='val_loss')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"model\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" input_1 (InputLayer) [(None, 10)] 0 \n",
" \n",
" token_and_position_embeddin (None, 10, 128) 38846080 \n",
" g (TokenAndPositionEmbeddin \n",
" g) \n",
" \n",
" transformer_decoder (Transf (None, 10, 128) 132480 \n",
" ormerDecoder) \n",
" \n",
" transformer_decoder_1 (Tran (None, 10, 128) 132480 \n",
" sformerDecoder) \n",
" \n",
" transformer_decoder_2 (Tran (None, 10, 128) 132480 \n",
" sformerDecoder) \n",
" \n",
" transformer_decoder_3 (Tran (None, 10, 128) 132480 \n",
" sformerDecoder) \n",
" \n",
" dropout (Dropout) (None, 10, 128) 0 \n",
" \n",
" dense (Dense) (None, 10, 303475) 39148275 \n",
" \n",
"=================================================================\n",
"Total params: 78,524,275\n",
"Trainable params: 78,524,275\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"Epoch 1/150\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-02-28 23:36:23.887413: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:630] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n",
"2023-02-28 23:36:24.308423: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x7ff6d67579b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
"2023-02-28 23:36:24.308518: I tensorflow/compiler/xla/service/service.cc:181] StreamExecutor device (0): NVIDIA GeForce RTX 3050 Laptop GPU, Compute Capability 8.6\n",
"2023-02-28 23:36:24.328912: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
"2023-02-28 23:36:24.549826: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"3082/8957 [=========>....................] - ETA: 55:03 - loss: 5.8952 - perplexity: 363.2977 - accuracy: 0.4296"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/media/yuvrajtalukdar/New Volume/computer/undergoing_projects/AssamWiki GPT/AssameseWikiGPT.ipynb Cell 9\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/media/yuvrajtalukdar/New%20Volume/computer/undergoing_projects/AssamWiki%20GPT/AssameseWikiGPT.ipynb#X11sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m model \u001b[39m=\u001b[39m create_model2(\u001b[39m4\u001b[39m)\n\u001b[1;32m <a href='vscode-notebook-cell:/media/yuvrajtalukdar/New%20Volume/computer/undergoing_projects/AssamWiki%20GPT/AssameseWikiGPT.ipynb#X11sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m model\u001b[39m.\u001b[39msummary()\n\u001b[0;32m----> <a href='vscode-notebook-cell:/media/yuvrajtalukdar/New%20Volume/computer/undergoing_projects/AssamWiki%20GPT/AssameseWikiGPT.ipynb#X11sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m history \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mfit(train_dataset,validation_data\u001b[39m=\u001b[39;49mvalid_dataset,epochs\u001b[39m=\u001b[39;49m\u001b[39m150\u001b[39;49m,callbacks\u001b[39m=\u001b[39;49m[sampler, reducelr])\n",
"File \u001b[0;32m~/miniconda3/envs/miniproject/lib/python3.10/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 67\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
"File \u001b[0;32m~/miniconda3/envs/miniproject/lib/python3.10/site-packages/keras/engine/training.py:1650\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1642\u001b[0m \u001b[39mwith\u001b[39;00m tf\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mexperimental\u001b[39m.\u001b[39mTrace(\n\u001b[1;32m 1643\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 1644\u001b[0m epoch_num\u001b[39m=\u001b[39mepoch,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1647\u001b[0m _r\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[1;32m 1648\u001b[0m ):\n\u001b[1;32m 1649\u001b[0m callbacks\u001b[39m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1650\u001b[0m tmp_logs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_function(iterator)\n\u001b[1;32m 1651\u001b[0m \u001b[39mif\u001b[39;00m data_handler\u001b[39m.\u001b[39mshould_sync:\n\u001b[1;32m 1652\u001b[0m context\u001b[39m.\u001b[39masync_wait()\n",
"File \u001b[0;32m~/miniconda3/envs/miniproject/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 149\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 152\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
"File \u001b[0;32m~/miniconda3/envs/miniproject/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:880\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 877\u001b[0m compiler \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mxla\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnonXla\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 879\u001b[0m \u001b[39mwith\u001b[39;00m OptionalXlaContext(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 880\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m 882\u001b[0m new_tracing_count \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m 883\u001b[0m without_tracing \u001b[39m=\u001b[39m (tracing_count \u001b[39m==\u001b[39m new_tracing_count)\n",
"File \u001b[0;32m~/miniconda3/envs/miniproject/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:912\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 909\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n\u001b[1;32m 910\u001b[0m \u001b[39m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m 911\u001b[0m \u001b[39m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 912\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_no_variable_creation_fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds) \u001b[39m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m 913\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_variable_creation_fn \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 914\u001b[0m \u001b[39m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m 915\u001b[0m \u001b[39m# in parallel.\u001b[39;00m\n\u001b[1;32m 916\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n",
"File \u001b[0;32m~/miniconda3/envs/miniproject/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:134\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[1;32m 132\u001b[0m (concrete_function,\n\u001b[1;32m 133\u001b[0m filtered_flat_args) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 134\u001b[0m \u001b[39mreturn\u001b[39;00m concrete_function\u001b[39m.\u001b[39;49m_call_flat(\n\u001b[1;32m 135\u001b[0m filtered_flat_args, captured_inputs\u001b[39m=\u001b[39;49mconcrete_function\u001b[39m.\u001b[39;49mcaptured_inputs)\n",
"File \u001b[0;32m~/miniconda3/envs/miniproject/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1745\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1741\u001b[0m possible_gradient_type \u001b[39m=\u001b[39m gradients_util\u001b[39m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m 1742\u001b[0m \u001b[39mif\u001b[39;00m (possible_gradient_type \u001b[39m==\u001b[39m gradients_util\u001b[39m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m 1743\u001b[0m \u001b[39mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m 1744\u001b[0m \u001b[39m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1745\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_build_call_outputs(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inference_function\u001b[39m.\u001b[39;49mcall(\n\u001b[1;32m 1746\u001b[0m ctx, args, cancellation_manager\u001b[39m=\u001b[39;49mcancellation_manager))\n\u001b[1;32m 1747\u001b[0m forward_backward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m 1748\u001b[0m args,\n\u001b[1;32m 1749\u001b[0m possible_gradient_type,\n\u001b[1;32m 1750\u001b[0m executing_eagerly)\n\u001b[1;32m 1751\u001b[0m forward_function, args_with_tangents \u001b[39m=\u001b[39m forward_backward\u001b[39m.\u001b[39mforward()\n",
"File \u001b[0;32m~/miniconda3/envs/miniproject/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:378\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 376\u001b[0m \u001b[39mwith\u001b[39;00m _InterpolateFunctionError(\u001b[39mself\u001b[39m):\n\u001b[1;32m 377\u001b[0m \u001b[39mif\u001b[39;00m cancellation_manager \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 378\u001b[0m outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39;49mexecute(\n\u001b[1;32m 379\u001b[0m \u001b[39mstr\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msignature\u001b[39m.\u001b[39;49mname),\n\u001b[1;32m 380\u001b[0m num_outputs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_num_outputs,\n\u001b[1;32m 381\u001b[0m inputs\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m 382\u001b[0m attrs\u001b[39m=\u001b[39;49mattrs,\n\u001b[1;32m 383\u001b[0m ctx\u001b[39m=\u001b[39;49mctx)\n\u001b[1;32m 384\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 385\u001b[0m outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m 386\u001b[0m \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msignature\u001b[39m.\u001b[39mname),\n\u001b[1;32m 387\u001b[0m num_outputs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_outputs,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 390\u001b[0m ctx\u001b[39m=\u001b[39mctx,\n\u001b[1;32m 391\u001b[0m cancellation_manager\u001b[39m=\u001b[39mcancellation_manager)\n",
"File \u001b[0;32m~/miniconda3/envs/miniproject/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:52\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 51\u001b[0m ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 52\u001b[0m tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39;49mTFE_Py_Execute(ctx\u001b[39m.\u001b[39;49m_handle, device_name, op_name,\n\u001b[1;32m 53\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 54\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 55\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"model = create_model2(4)\n",
"model.summary()\n",
"history = model.fit(train_dataset,validation_data=valid_dataset,epochs=150,callbacks=[sampler, reducelr])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def sample_token(logits):\n",
" logits, indices = tf.math.top_k(logits, k=5, sorted=True)\n",
" indices = np.asarray(indices).astype(\"int32\")\n",
" preds = keras.activations.softmax(tf.expand_dims(logits, 0))[0]\n",
" preds = np.asarray(preds).astype(\"float32\")\n",
" return np.random.choice(indices, p=preds)\n",
"\n",
"def generate_text(prompt, response_length=50):\n",
" decoded_sample = prompt\n",
" for i in range(response_length-1):\n",
" tokenized_prompt = vectorize_layer([decoded_sample])[:, :-1]\n",
" predictions = model.predict([tokenized_prompt], verbose=0)\n",
" sample_index = len(decoded_sample.strip().split())-1\n",
"\n",
" sampled_token = sample_token(predictions[0][sample_index])\n",
" sampled_token = index_lookup[sampled_token]\n",
" decoded_sample += \" \" + sampled_token\n",
" return decoded_sample"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"model.save(\"pd_plaintext_transformer.h5\")\n",
"pickle.dump(model, open('pd_plaintext_transformer.pkl', 'wb'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"generate_text('য়ুৰিৰ দাদাক আৰু ',response_length=50)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "miniproject",
"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": "3.10.4"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "b18115e74db522ea4edaf3f03801a60154dbaca70e4a91a6289c29c6971e06fa"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|