Bose_Hubbard_precalc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "prerequisites:  \n",
    "tenpy: https://tenpy.github.io/INSTALL.html  \n",
    "tensorflow: https://www.tensorflow.org/install  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-23T21:08:39.332372Z",
     "start_time": "2020-01-23T21:08:34.367233Z"
    }
   },
   "outputs": [],
   "source": [
    "from AD_tools import *\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.colors import BoundaryNorm\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential,Model\n",
    "from tensorflow.keras.layers import Input,Dense,Conv1D,MaxPooling1D,UpSampling1D,Flatten,add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-23T21:08:39.397080Z",
     "start_time": "2020-01-23T21:08:39.390407Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.12.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-23T21:08:40.459019Z",
     "start_time": "2020-01-23T21:08:40.453068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tenpy\n",
    "tenpy.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data\n",
    "This was previously calcualted on a separate session for 130x100 states for the whole phase diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-23T21:08:43.601031Z",
     "start_time": "2020-01-23T21:08:43.597105Z"
    }
   },
   "outputs": [],
   "source": [
    "Us = np.concatenate([np.linspace(0.,2.,30),np.linspace(2.,8,100)])\n",
    "Vs = np.linspace(0,5,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13000, 100, 20)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test2D = np.concatenate([np.load(\"test_data/x_test2D_{}.npz\".format(i),allow_pickle=True)[\"x_test2D\"] for i in range(10)])\n",
    "x_test2D.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(picking 20 ES is arbitrary, also works with 1 or all of them)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scc_cnn1D(loss,optimizer,activation0,activation,x_shape):\n",
    "    in_dim = x_shape\n",
    "    input_img = Input(shape=in_dim)\n",
    "   \n",
    "    x1 = Conv1D(64, 3, activation=activation0, padding='same')(input_img) # 100 100 64\n",
    "    x2 = MaxPooling1D(2, padding='same')(x1) # 50 50 64\n",
    "    x2 = Conv1D(64, 3, activation=activation0, padding='same')(x2) # 50 50 128\n",
    "    x3 = MaxPooling1D(2, padding='same')(x2) # 25 25 128\n",
    "    encoded = Conv1D(64, 3, activation=activation0, padding='same')(x3) # 25 25 128\n",
    "\n",
    "    y=UpSampling1D(2)(encoded) # 50 50 128\n",
    "    y=add([x2,y]) # 50 50 128\n",
    "    y=Conv1D(64, 3, activation=activation0, padding='same')(y) # 50 50 128\n",
    "    y=UpSampling1D(2)(y) # 100 100 128\n",
    "    y=add([x1,y]) # 100 100 128\n",
    "    decoded = Conv1D(x_shape[-1], 3, activation=activation, padding='same')(y) # 100 100 4\n",
    "\n",
    "    cnn = Model(input_img, decoded)\n",
    "\n",
    "    cnn.compile(loss=loss,optimizer=optimizer)#,metrics=['accuracy']) #adadelta\n",
    "    return cnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 64, 1)        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d (Conv1D)                 (None, 64, 64)       256         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D)    (None, 32, 64)       0           conv1d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 32, 64)       12352       max_pooling1d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1D)  (None, 16, 64)       0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 16, 64)       12352       max_pooling1d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling1d (UpSampling1D)    (None, 32, 64)       0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 32, 64)       0           conv1d_1[0][0]                   \n",
      "                                                                 up_sampling1d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 32, 64)       12352       add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling1d_1 (UpSampling1D)  (None, 64, 64)       0           conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 64, 64)       0           conv1d[0][0]                     \n",
      "                                                                 up_sampling1d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 64, 1)        193         add_1[0][0]                      \n",
      "==================================================================================================\n",
      "Total params: 37,505\n",
      "Trainable params: 37,505\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn = scc_cnn1D(\"mse\",\"adam\",\"relu\",\"relu\",(64,1,))\n",
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training\n",
    "Usually compute extra training set from test set, but since test set is big enough we can just use parts of the test set for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pick_Us(Umin,Umax):\n",
    "    return np.arange(np.where(Us>Umin)[0][0]-1,np.where(Us<Umax)[0][-1]+1,1)    \n",
    "def pick_Vs(Vmin,Vmax):\n",
    "    return np.arange(np.where(Vs>Vmin)[0][0]-1,np.where(Vs<Vmax)[0][-1]+1,1)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(494, 100, 20)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trains0 = [[(0,1.3),(0,1.3)]]\n",
    "x_train = []\n",
    "for train in trains0:\n",
    "    x_train.append(x_test2D.reshape(130,100,*x_test2D.shape[1:])[pick_Us(*train[1])][:,pick_Vs(*train[0])].reshape(-1,*x_test2D.shape[1:]))\n",
    "x_train = np.concatenate(x_train)\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_loss2(y_true,y_pred):\n",
    "    return tf.keras.backend.sqrt(tf.keras.backend.sum((y_true - y_pred)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 494 samples, validate on 494 samples\n",
      "Epoch 1/10\n",
      "494/494 [==============================] - 3s 5ms/step - loss: 4.8488 - val_loss: 1.4604\n",
      "Epoch 2/10\n",
      "494/494 [==============================] - 0s 695us/step - loss: 0.9614 - val_loss: 0.6107\n",
      "Epoch 3/10\n",
      "494/494 [==============================] - 0s 665us/step - loss: 0.4920 - val_loss: 0.3859\n",
      "Epoch 4/10\n",
      "494/494 [==============================] - 0s 697us/step - loss: 0.3330 - val_loss: 0.2837\n",
      "Epoch 5/10\n",
      "494/494 [==============================] - 0s 630us/step - loss: 0.2418 - val_loss: 0.2068\n",
      "Epoch 6/10\n",
      "494/494 [==============================] - 0s 689us/step - loss: 0.2209 - val_loss: 0.1862\n",
      "Epoch 7/10\n",
      "494/494 [==============================] - 0s 652us/step - loss: 0.1658 - val_loss: 0.1381\n",
      "Epoch 8/10\n",
      "494/494 [==============================] - 0s 637us/step - loss: 0.1418 - val_loss: 0.1356\n",
      "Epoch 9/10\n",
      "494/494 [==============================] - 0s 650us/step - loss: 0.1377 - val_loss: 0.1034\n",
      "Epoch 10/10\n",
      "494/494 [==============================] - 0s 629us/step - loss: 0.1261 - val_loss: 0.0974\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn0 = training(x_train, scc_cnn1D, name = \"\", provide_cnn = False,\n",
    "             load_prev = False, num_epochs = 10,  verbose_val = 1, batch_size = 24, shuffle = True, early = False,\n",
    "             loss = train_loss2, activation0 = 'relu', activation = 'tanh', optimizer = \"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = 100*eval_loss(x_test2D,cnn0.predict(x_test2D),norm=norm2)/eval_loss(x_test2D,0*x_test2D,norm=norm2)\n",
    "out = out.reshape(130,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = np.meshgrid(Vs,Us)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as patches\n",
    "cmap = plt.get_cmap(\"plasma\") #'viridis'\n",
    "fig,axs = plt.subplots(figsize=(8,5),squeeze=False)\n",
    "\n",
    "nbins=100\n",
    "\n",
    "ax = axs[0,0]\n",
    "levels = MaxNLocator(nbins=nbins).tick_values(out.min(),out.max()) #out.max()\n",
    "norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n",
    "im = ax.pcolormesh(x, y, out, cmap=cmap, norm=norm)\n",
    "cbar = fig.colorbar(im, ax=ax)\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "ax.set_title(\"Loss [%]\",fontsize=20)\n",
    "ax.set_xlabel(\"V\",fontsize=24)\n",
    "ax.set_ylabel(\"U\",fontsize=24)\n",
    "ax.tick_params(labelsize=20)\n",
    "#ax.set_ylim(*ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'U = 5.030')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(ncols=3,figsize=(18,5))\n",
    "ax = axs[0]\n",
    "col=6\n",
    "ax.plot(y[:,col],out[:,col])\n",
    "ax.set_title(\"V = {:.3f}\".format(x[0,col]))\n",
    "\n",
    "ax = axs[1]\n",
    "row = 6\n",
    "ax.plot(x[row],out[row])\n",
    "ax.set_title(\"U = {:.3f}\".format(y[row,0]))\n",
    "\n",
    "ax = axs[2]\n",
    "row = 80\n",
    "ax.plot(x[row],out[row])\n",
    "ax.set_title(\"U = {:.3f}\".format(y[row,0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.9"
  },
  "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
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}