GANdemo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-08T23:15:22.414470Z",
     "start_time": "2019-08-08T23:15:17.857221Z"
    }
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import os\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "mb_size = 128\n",
    "Z_dim = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-08T07:28:52.183097Z",
     "start_time": "2019-08-08T07:28:38.608442Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-1-e627224ef189>:13: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "Extracting ./data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting ./data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets('./data/MNIST_data', one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-08T07:28:52.850101Z",
     "start_time": "2019-08-08T07:28:52.190346Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "def weight_var(shape, name):\n",
    "    return tf.get_variable(name=name, shape=shape, initializer=tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "\n",
    "def bias_var(shape, name):\n",
    "    return tf.get_variable(name=name, shape=shape, initializer=tf.constant_initializer(0))\n",
    "\n",
    "\n",
    "# discriminater net\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, 784], name='X')\n",
    "\n",
    "D_W1 = weight_var([784, 128], 'D_W1')\n",
    "D_b1 = bias_var([128], 'D_b1')\n",
    "\n",
    "D_W2 = weight_var([128, 1], 'D_W2')\n",
    "D_b2 = bias_var([1], 'D_b2')\n",
    "\n",
    "\n",
    "theta_D = [D_W1, D_W2, D_b1, D_b2]\n",
    "\n",
    "\n",
    "# generator net\n",
    "\n",
    "Z = tf.placeholder(tf.float32, shape=[None, 100], name='Z')\n",
    "\n",
    "G_W1 = weight_var([100, 128], 'G_W1')\n",
    "G_b1 = bias_var([128], 'G_B1')\n",
    "\n",
    "G_W2 = weight_var([128, 784], 'G_W2')\n",
    "G_b2 = bias_var([784], 'G_B2')\n",
    "\n",
    "theta_G = [G_W1, G_W2, G_b1, G_b2]\n",
    "def generator(z):\n",
    "    G_h1 = tf.nn.relu(tf.matmul(z, G_W1) + G_b1)\n",
    "    G_log_prob = tf.matmul(G_h1, G_W2) + G_b2\n",
    "    G_prob = tf.nn.sigmoid(G_log_prob)\n",
    "\n",
    "    return G_prob\n",
    "\n",
    "\n",
    "def discriminator(x):\n",
    "    D_h1 = tf.nn.relu(tf.matmul(x, D_W1) + D_b1)\n",
    "    D_logit = tf.matmul(D_h1, D_W2) + D_b2\n",
    "    D_prob = tf.nn.sigmoid(D_logit)\n",
    "    return D_prob, D_logit\n",
    "\n",
    "G_sample = generator(Z)\n",
    "D_real, D_logit_real = discriminator(X)\n",
    "D_fake, D_logit_fake = discriminator(G_sample)\n",
    "D_loss = -tf.reduce_mean(tf.log(D_real) + tf.log(1. - D_fake))\n",
    "G_loss = -tf.reduce_mean(tf.log(D_fake))\n",
    "\n",
    "D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\n",
    "    logits=D_logit_real, labels=tf.ones_like(D_logit_real)))\n",
    "D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\n",
    "    logits=D_logit_fake, labels=tf.zeros_like(D_logit_fake)))\n",
    "D_loss = D_loss_real + D_loss_fake\n",
    "G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\n",
    "    logits=D_logit_fake, labels=tf.ones_like(D_logit_fake)))\n",
    "D_optimizer = tf.train.AdamOptimizer().minimize(D_loss, var_list=theta_D)\n",
    "G_optimizer = tf.train.AdamOptimizer().minimize(G_loss, var_list=theta_G)\n",
    "def sample_Z(m, n):\n",
    "    '''Uniform prior for G(Z)'''\n",
    "    return np.random.uniform(-1., 1., size=[m, n])\n",
    "\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-08T07:30:39.628853Z",
     "start_time": "2019-08-08T07:28:52.853135Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 0 D loss: 1.501 G_loss: 2.398\n",
      "Iter: 1000 D loss: 0.02053 G_loss: 6.749\n",
      "Iter: 2000 D loss: 0.07285 G_loss: 5.144\n",
      "Iter: 3000 D loss: 0.1927 G_loss: 5.546\n",
      "Iter: 4000 D loss: 0.1536 G_loss: 4.8\n",
      "Iter: 5000 D loss: 0.2518 G_loss: 4.739\n",
      "Iter: 6000 D loss: 0.2418 G_loss: 3.371\n",
      "Iter: 7000 D loss: 0.4397 G_loss: 3.515\n",
      "Iter: 8000 D loss: 0.3669 G_loss: 2.932\n",
      "Iter: 9000 D loss: 0.4245 G_loss: 3.447\n"
     ]
    }
   ],
   "source": [
    "for it in range(10000):\n",
    "    X_mb, _ = mnist.train.next_batch(mb_size)\n",
    "\n",
    "    _, D_loss_curr = sess.run([D_optimizer, D_loss], feed_dict={\n",
    "                              X: X_mb, Z: sample_Z(mb_size, Z_dim)})\n",
    "    _, G_loss_curr = sess.run([G_optimizer, G_loss], feed_dict={\n",
    "                              Z: sample_Z(mb_size, Z_dim)})\n",
    "\n",
    "    if it % 1000 == 0:\n",
    "        print('Iter: {}'.format(it),\n",
    "              'D loss: {:.4}'.format(D_loss_curr), \n",
    "              'G_loss: {:.4}'.format(G_loss_curr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-08T16:15:55.268637Z",
     "start_time": "2019-08-08T16:15:55.122431Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-8-c0d77d66cdab>:15: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "[[5 4 3 0 0]\n",
      " [2 3 0 0 0]\n",
      " [2 3 5 4 2]]\n"
     ]
    }
   ],
   "source": [
    "def get_shape(tensor):\n",
    "    static_shape = tensor.shape.as_list()\n",
    "    dynamic_shape = tf.unstack(tf.shape(tensor))\n",
    "    dims = [s[1] if s[0] is None else s[0]\n",
    "          for s in zip(static_shape, dynamic_shape)]\n",
    "    return dims\n",
    "\n",
    "choose = tf.placeholder(tf.int64,[None,5])\n",
    "minValue = tf.placeholder(tf.int64,[None,1])\n",
    "ex=6\n",
    "feed_dict = {\n",
    "    choose:[[5,4,3,0,1],[2,3,0,4,2],[2,3,5,4,2]],\n",
    "    minValue:[[3],[2],[2]]}\n",
    "\n",
    "x = tf.tile(tf.reshape(tf.arg_max(tf.cast(choose<minValue,tf.int64),1),(-1,1)),[1,5])\n",
    "y = tf.tile(tf.reduce_sum(tf.cast(choose<minValue,tf.int64),axis=1,keep_dims=True),[1,5])\n",
    "index = tf.tile(tf.expand_dims(tf.range(5,dtype=tf.int64),0),[get_shape(choose)[0],1])\n",
    "result = tf.where(index<x,choose,tf.zeros_like(choose))\n",
    "result = tf.where(index<x,choose,tf.zeros_like(choose)) + tf.where(tf.equal(y,0),choose,tf.zeros_like(choose))\n",
    "sess =tf.Session()\n",
    "def see(x):\n",
    "    print(sess.run(x, feed_dict))\n",
    "\n",
    "see(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-09T00:30:29.541458Z",
     "start_time": "2019-08-09T00:30:29.445382Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, 6, 5)\n",
      "[[[5 4 3 0 1]\n",
      "  [2 3 0 4 2]\n",
      "  [2 3 5 4 2]\n",
      "  [0 0 0 0 0]\n",
      "  [0 0 0 0 0]\n",
      "  [0 0 0 0 0]]]\n"
     ]
    }
   ],
   "source": [
    "a = tf.placeholder(tf.int32,[None, None, 5])\n",
    "\n",
    "\n",
    "batch = tf.shape(a)[0]\n",
    "size = tf.shape(a)[1]\n",
    "\n",
    "b = tf.zeros([batch, 6, 5], dtype=tf.int32)\n",
    "\n",
    "d = tf.slice(a, [0, 0, 0], [batch, size, 5])\n",
    "e = tf.slice(b, [0, 0, 0], [batch, 6-size, 5])\n",
    "f = tf.concat([d,e],1)\n",
    "g = tf.reshape(f, [-1, 6, 5])\n",
    "print(g.shape)\n",
    "print(sess.run(g, {a:[[[5,4,3,0,1],[2,3,0,4,2],[2,3,5,4,2]]]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-08T23:28:53.748474Z",
     "start_time": "2019-08-08T23:28:53.378116Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"concat:0\", shape=(2, 5), dtype=float32)\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "(array([[-0.8113182 ,  1.4845988 ,  0.06532937],\n",
      "       [-2.4427042 ,  0.0992484 ,  0.5912243 ]], dtype=float32), array([[ 0.7,  0.9,  0. ,  0. ,  0. ],\n",
      "       [80. , 90. ,  0. ,  0. ,  0. ]], dtype=float32))\n",
      "<tf.Variable 'Variable_1:0' shape=(2, 5) dtype=float32_ref>\n",
      "[array([], shape=(2, 0), dtype=float32), array([[1.],\n",
      "       [1.]], dtype=float32), array([[ 0.9,  0. ,  0. ,  0. ],\n",
      "       [90. ,  0. ,  0. ,  0. ]], dtype=float32), array([[ 1. ,  0.9,  0. ,  0. ,  0. ],\n",
      "       [ 1. , 90. ,  0. ,  0. ,  0. ]], dtype=float32)]\n",
      "[array([[ 0.7],\n",
      "       [80. ]], dtype=float32), array([[1.],\n",
      "       [1.]], dtype=float32), array([[0., 0., 0.],\n",
      "       [0., 0., 0.]], dtype=float32), array([[ 0.7,  1. ,  0. ,  0. ,  0. ],\n",
      "       [80. ,  1. ,  0. ,  0. ,  0. ]], dtype=float32)]\n",
      "[array([[ 0.7,  0.9],\n",
      "       [80. , 90. ]], dtype=float32), array([[1.],\n",
      "       [1.]], dtype=float32), array([[0., 0.],\n",
      "       [0., 0.]], dtype=float32), array([[ 0.7,  0.9,  1. ,  0. ,  0. ],\n",
      "       [80. , 90. ,  1. ,  0. ,  0. ]], dtype=float32)]\n",
      "[array([[ 0.7,  0.9,  0. ],\n",
      "       [80. , 90. ,  0. ]], dtype=float32), array([[1.],\n",
      "       [1.]], dtype=float32), array([[0.],\n",
      "       [0.]], dtype=float32), array([[ 0.7,  0.9,  0. ,  1. ,  0. ],\n",
      "       [80. , 90. ,  0. ,  1. ,  0. ]], dtype=float32)]\n",
      "[array([[ 0.7,  0.9,  0. ,  0. ],\n",
      "       [80. , 90. ,  0. ,  0. ]], dtype=float32), array([[1.],\n",
      "       [1.]], dtype=float32), array([], shape=(2, 0), dtype=float32), array([[ 0.7,  0.9,  0. ,  0. ,  1. ],\n",
      "       [80. , 90. ,  0. ,  0. ,  1. ]], dtype=float32)]\n",
      "[array([], shape=(2, 0), dtype=float32), array([[1.],\n",
      "       [1.]], dtype=float32), array([[ 0.9,  0. ,  0. ,  0. ],\n",
      "       [90. ,  0. ,  0. ,  0. ]], dtype=float32), array([[ 1. ,  0.9,  0. ,  0. ,  0. ],\n",
      "       [ 1. , 90. ,  0. ,  0. ,  0. ]], dtype=float32)]\n",
      "[array([[1.],\n",
      "       [1.]], dtype=float32), array([[1.],\n",
      "       [1.]], dtype=float32), array([[0., 0., 0.],\n",
      "       [0., 0., 0.]], dtype=float32), array([[1., 1., 0., 0., 0.],\n",
      "       [1., 1., 0., 0., 0.]], dtype=float32)]\n",
      "[array([[1., 1.],\n",
      "       [1., 1.]], dtype=float32), array([[1.],\n",
      "       [1.]], dtype=float32), array([[0., 0.],\n",
      "       [0., 0.]], dtype=float32), array([[1., 1., 1., 0., 0.],\n",
      "       [1., 1., 1., 0., 0.]], dtype=float32)]\n",
      "[array([[1., 1., 1.],\n",
      "       [1., 1., 1.]], dtype=float32), array([[1.],\n",
      "       [1.]], dtype=float32), array([[0.],\n",
      "       [0.]], dtype=float32), array([[1., 1., 1., 1., 0.],\n",
      "       [1., 1., 1., 1., 0.]], dtype=float32)]\n",
      "[array([[1., 1., 1., 1.],\n",
      "       [1., 1., 1., 1.]], dtype=float32), array([[1.],\n",
      "       [1.]], dtype=float32), array([], shape=(2, 0), dtype=float32), array([[1., 1., 1., 1., 1.],\n",
      "       [1., 1., 1., 1., 1.]], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "x = tf.placeholder(tf.float32, shape=(2, 2), name=\"input\")\n",
    "xx=tf.zeros([2,3],tf.float32)\n",
    "xx2=tf.concat([x,xx],axis=1)\n",
    "columnTensor=tf.ones([2,1],tf.float32)\n",
    "print (xx2)\n",
    "w1= tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1),trainable=False)\n",
    "w2= tf.Variable(xx2,trainable=False)\n",
    "#编写程序修改Tensor矩阵的某一列\n",
    "embed_size=300\n",
    "max_sentence_length=50\n",
    "max_node_size=max_sentence_length*2-1#训练语料库句子节点的最大长度。注意，是句子节点的最大长度。不是句子单词的最大数目。\n",
    "def modify_one_column(tensor,columnTensor,index):#index也是tensor\n",
    "#tensor为二维矩阵\n",
    "#columnTensor的维度就是tensor中的一列\n",
    "    numlines=tensor.shape[0].value #行数\n",
    "    numcolunms=tensor.shape[1].value #列数\n",
    "    new_tensor_left=tf.slice(tensor, [0, 0], [numlines, index])\n",
    "    new_tensor_right=tf.slice(tensor, [0, index+1], [numlines, numcolunms-(index+1)])\n",
    "    new_tensor=tf.concat([new_tensor_left,columnTensor,new_tensor_right],1)\n",
    "    return new_tensor_left,new_tensor_right,new_tensor\n",
    "\n",
    "sess = tf.Session()\n",
    "init_op = tf.global_variables_initializer()  \n",
    "#print(sess.run(x, feed_dict={x: [[0.7,0.9]]}))\n",
    "sess.run(init_op,feed_dict={x: [[0.7,0.9],[80.0,90.0]]})\n",
    "print (sess.run((w1,w2)))\n",
    "print (w2)\n",
    "for index in range(5):\n",
    "    index_tensor=tf.constant(index,tf.int32)\n",
    "    new_tensor_left,new_tensor_right,w22=modify_one_column(w2,columnTensor,index_tensor)\n",
    "    print (sess.run([new_tensor_left,columnTensor,new_tensor_right,w22]))\n",
    "for index in range(5):\n",
    "    index_tensor=tf.constant(index,tf.int32)\n",
    "    new_tensor_left,new_tensor_right,w2=modify_one_column(w2,columnTensor,index_tensor)\n",
    "    print (sess.run([new_tensor_left,columnTensor,new_tensor_right,w2]))\n",
    "#print (sess.run(w2,feed_dict={x: [[0.7,0.9]]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-08T16:26:39.843324Z",
     "start_time": "2019-08-08T16:26:39.809008Z"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not broadcast input array from shape (1,3,5) into shape (1,6,5)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-f589b0147a8a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (1,3,5) into shape (1,6,5)"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([[[5,4,3,0,1],[2,3,0,4,2],[2,3,5,4,2]]])\n",
    "b = np.zeros([1, 6, 5])\n",
    "b[:,:,:]=a[:,:,:]\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-08T07:30:39.638169Z",
     "start_time": "2019-08-08T07:30:39.631192Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot(samples):\n",
    "    fig = plt.figure(figsize=(4, 4))\n",
    "    gs = gridspec.GridSpec(4, 4)\n",
    "    gs.update(wspace=0.05, hspace=0.05)\n",
    "\n",
    "    for i, sample in enumerate(samples):  # [i,samples[i]] imax=16\n",
    "        ax = plt.subplot(gs[i])\n",
    "        plt.axis('off')\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_aspect('equal')\n",
    "        plt.imshow(sample.reshape(28, 28), cmap='Greys_r')\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-08T07:30:39.681779Z",
     "start_time": "2019-08-08T07:30:39.644289Z"
    }
   },
   "outputs": [],
   "source": [
    "if not os.path.exists('out/'):\n",
    "    os.makedirs('out/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-08T09:26:24.571056Z",
     "start_time": "2019-08-08T07:30:39.689127Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-f903891985af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m         samples = sess.run(G_sample, feed_dict={\n\u001b[1;32m      7\u001b[0m                            Z: sample_Z(16, Z_dim)})  # 16*784\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'out/{}.png'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tight'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mi\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-4-709c49e001c1>\u001b[0m in \u001b[0;36mplot\u001b[0;34m(samples)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# [i,samples[i]] imax=16\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'off'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msubplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1082\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1083\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1084\u001b[0;31m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1085\u001b[0m     \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1086\u001b[0m     \u001b[0mbyebye\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1365\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m             \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1368\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1369\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axes/_subplots.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m         \u001b[0;31m# _axes_class is set in the subplot_class_factory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axes_class\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;31m# add a layout box to this, for both the full axis, and the poss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;31m# of the axis.  We need both because the axes may become smaller\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, rect, facecolor, frameon, sharex, sharey, label, xscale, yscale, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterization_zorder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_connected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m  \u001b[0;31m# a dict from events to (id, func)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m         \u001b[0;31m# funcs used to format x and y - fall back on major formatters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mcla\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1094\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis_on\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1095\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1096\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1097\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1098\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mset_clip_path\u001b[0;34m(self, clippath, transform)\u001b[0m\n\u001b[1;32m    923\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclippath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    924\u001b[0m         \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mArtist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclippath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 925\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajorTicks\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    926\u001b[0m             \u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclippath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    927\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, instance, cls)\u001b[0m\n\u001b[1;32m    680\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    681\u001b[0m                 \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 682\u001b[0;31m                 \u001b[0mtick\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    683\u001b[0m                 \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    684\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_tick\u001b[0;34m(self, major)\u001b[0m\n\u001b[1;32m   1816\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1817\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1818\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mXTick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1819\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1820\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kw)\u001b[0m\n\u001b[1;32m    174\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick1line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick1line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick2line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick2line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 176\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgridline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_gridline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_text1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_gridline\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    497\u001b[0m                           \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grid_alpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m                           \u001b[0mmarkersize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 499\u001b[0;31m                           **self._grid_kw)\n\u001b[0m\u001b[1;32m    500\u001b[0m         \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_xaxis_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwhich\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'grid'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m         \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGRIDLINE_INTERPOLATION_STEPS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_color\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_marker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMarkerStyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmarker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfillstyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_markevery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/markers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, marker, fillstyle)\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_marker_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_fillstyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfillstyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_marker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmarker\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_recache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/markers.py\u001b[0m in \u001b[0;36mset_marker\u001b[0;34m(self, marker)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_marker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/markers.py\u001b[0m in \u001b[0;36m_recache\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    249\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_empty_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transform\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIdentityTransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alt_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alt_transform\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "i = 0\n",
    "for it in range(1000000):\n",
    "    if it % 1000 == 0:\n",
    "        samples = sess.run(G_sample, feed_dict={\n",
    "                           Z: sample_Z(16, Z_dim)})  # 16*784\n",
    "        fig = plot(samples)\n",
    "        plt.savefig('out/{}.png'.format(str(i).zfill(3)), bbox_inches='tight')\n",
    "        i += 1\n",
    "        plt.close(fig)\n",
    "\n",
    "    X_mb, _ = mnist.train.next_batch(mb_size)\n",
    "\n",
    "    _, D_loss_curr = sess.run([D_optimizer, D_loss], feed_dict={\n",
    "                              X: X_mb, Z: sample_Z(mb_size, Z_dim)})\n",
    "    _, G_loss_curr = sess.run([G_optimizer, G_loss], feed_dict={\n",
    "                              Z: sample_Z(mb_size, Z_dim)})\n",
    "\n",
    "    if it % 1000 == 0:\n",
    "        print('Iter: {}'.format(it),\n",
    "              'D loss: {:.4}'.format(D_loss_curr), \n",
    "              'G_loss: {:.4}'.format(G_loss_curr))"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.0"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}