7_tensorflow_模型的评估.ipynb

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    "tf.argmax与numpy的argmax函数作用一样，作用都是取行或者列的最大值的位置"
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   "source": [
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
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    "classes = np.array([[1,0,0],\n",
    "                   [0,1,0],\n",
    "                   [0,0,1]])\n",
    "logits = np.array([[0.9,0.1,0],\n",
    "                  [0.6,0.8,0.9],\n",
    "                  [0,0,1]])"
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    "np.argmax(logits,axis=1)"
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    "classes[np.argmax(logits,axis=1)]"
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    {
     "data": {
      "text/plain": [
       "array([ True, False,  True])"
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     "execution_count": 5,
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   "source": [
    " # 判断是否与预测相同\n",
    "np.equal(np.argmax(logits,axis=1),np.argmax(classes,axis=1))"
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   "outputs": [],
   "source": [
    "# tensorflow\n",
    "correct_prediction = tf.equal(tf.argmax(logits,1),tf.argmax(classes,1))"
   ]
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   "execution_count": 8,
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      "0.6666667\n"
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   "source": [
    "# 上面代码给出一组bool值，把布尔值转换成浮点数\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\n",
    "with tf.Session() as sess:\n",
    "    print(sess.run(accuracy))"
   ]
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   "execution_count": null,
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