examples/tutorial/TextCNNClassifier.ipynb

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  {
   "cell_type": "markdown",
   "id": "invalid-animation",
   "metadata": {},
   "source": [
    "# TextCNNClassifier\n",
    "\n",
    "可用的中文预训练参数：暂无"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "impossible-professor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v2.5.0\n"
     ]
    }
   ],
   "source": [
    "import uf\n",
    "\n",
    "print(uf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "minimal-cambodia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uf.TextCNNClassifier(\n",
      "    vocab_file=\"../../ref/vocab.txt\",\n",
      "    max_seq_length=128,\n",
      "    label_size=None,\n",
      "    init_checkpoint=None,\n",
      "    output_dir=None,\n",
      "    gpu_ids=\"0\",\n",
      "    filter_sizes=\"2,4,6\",\n",
      "    num_channels=6,\n",
      "    hidden_size=256,\n",
      "    do_lower_case=True,\n",
      "    truncate_method=\"LIFO\",\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = uf.TextCNNClassifier(\"../../ref/vocab.txt\", gpu_ids=\"0\")\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "forty-marathon",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [\"天亮以前说再见\", \"笑着泪流满面\", \"去迎接应该你的\", \"更好的明天\"]\n",
    "y = [1, 0, 2, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "looking-attempt",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "sharing-macintosh",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\Luv_d\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:1176: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "INFO:tensorflow:Build graph with 16,281,825 parameters (among which 5,427,275 are trainable)\n",
      "INFO:tensorflow:Running local_init_op\n",
      "INFO:tensorflow:Done running local_init_op\n",
      "INFO:tensorflow:Running training on 4 samples (step 0 -> 20)\n",
      "INFO:tensorflow:step 1, accuracy 0.2500, loss 1.103519, 6.12 steps/sec, 24.46 examples/sec\n",
      "INFO:tensorflow:step 4, accuracy 0.7500, loss 0.582421, 28.88 steps/sec, 115.54 examples/sec\n",
      "INFO:tensorflow:step 7, accuracy 1.0000, loss 0.080866, 26.82 steps/sec, 107.29 examples/sec\n",
      "INFO:tensorflow:step 10, accuracy 1.0000, loss 0.096210, 24.48 steps/sec, 97.92 examples/sec\n",
      "INFO:tensorflow:step 13, accuracy 1.0000, loss 0.000033, 20.54 steps/sec, 82.17 examples/sec\n",
      "INFO:tensorflow:step 16, accuracy 1.0000, loss 0.000003, 21.19 steps/sec, 84.75 examples/sec\n",
      "INFO:tensorflow:step 18, accuracy 1.0000, loss 0.000004, 19.25 steps/sec, 76.99 examples/sec\n",
      "INFO:tensorflow:step 20, accuracy 1.0000, loss 0.000252, 21.41 steps/sec, 85.64 examples/sec\n"
     ]
    }
   ],
   "source": [
    "model.fit(X, y, total_steps=20, learning_rate=0.01)     # 模型较小，可以适当提高学习率"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "studied-mechanism",
   "metadata": {},
   "source": [
    "# 推理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "funky-diversity",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running inference on 4 samples\n",
      "INFO:tensorflow:process 100.0%, 126.93 examples/sec\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'preds': [1, 0, 2, 0],\n",
       " 'probs': array([[1.9521560e-03, 9.9782097e-01, 2.2688659e-04],\n",
       "        [9.9999988e-01, 1.2660193e-08, 6.8851620e-08],\n",
       "        [3.9597539e-09, 1.1635332e-10, 1.0000000e+00],\n",
       "        [9.9992132e-01, 3.3130198e-06, 7.5329801e-05]], dtype=float32)}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "every-professor",
   "metadata": {},
   "source": [
    "# 评分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "great-alpha",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running scoring on 4 samples\n",
      "INFO:tensorflow:process 100.0%, 163.93 examples/sec\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 1.0, 'loss': 2.2112392e-05}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X, y)"
   ]
  }
 ],
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