极客 MNIST预测数字入门.ipynb

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     "text": [
      "Extracting data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
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   "source": [
    "#  MNIST机器学习入门   http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/mnist_beginners.html\n",
    "\n",
    "\n",
    "import input_data    # 自己路径下的input_data.py\n",
    "mnist = input_data.read_data_sets(\"data/MNIST_data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "W = tf.Variable(tf.zeros([784,10]))\n",
    "b = tf.Variable(tf.zeros([10]))\n",
    "y = tf.nn.softmax(tf.matmul(x,W) + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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   "source": [
    "y_ = tf.placeholder(\"float\", [None,10])\n",
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y))\n",
    "train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)\n",
    "init = tf.global_variables_initializer()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "source": [
    "sess = tf.Session()\n",
    "sess.run(init)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
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   "source": [
    "# 这里我们让模型循环训练1000次！\n",
    "for i in range(1000):\n",
    "  batch_xs, batch_ys = mnist.train.next_batch(100)\n",
    "  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})"
   ]
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "0.8759\n"
     ]
    }
   ],
   "source": [
    "# 评估我们的模型\n",
    "\n",
    "\n",
    "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "print( sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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