Training v10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setGPU: Setting GPU to: 0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from model import *\n",
    "import tensorflow as tf\n",
    "import setGPU\n",
    "#strategy = tf.distribute.MirroredStrategy([\"GPU:0\", \"GPU:1\"])\n",
    "\n",
    "imgs = np.load('../dataset/cv/train/x/generated_text_2.npz')['arr_0']\n",
    "labels = np.load('../dataset/cv/train/y/generated_labels_2.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "characters = set(char for label in labels for char in label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping characters to integers\n",
    "char_to_num = layers.experimental.preprocessing.StringLookup(\n",
    "                  vocabulary=sorted(list(characters)), num_oov_indices=0, mask_token=None )\n",
    "\n",
    "# Mapping integers back to original characters\n",
    "num_to_char = layers.experimental.preprocessing.StringLookup(\n",
    "                  vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs = np.transpose(imgs, (0, 2, 1, 3))\n",
    "'''\n",
    "max_len = max([len(label) for label in labels])\n",
    "encoded_labels = []\n",
    "for label in labels:\n",
    "    if label:\n",
    "        tmp = np.array([char_to_num(char) for char in label])\n",
    "        encoded_labels.append(np.pad(tmp, (0, max_len-tmp.shape[0]), constant_values=0))\n",
    "    else:\n",
    "        tmp = np.array([char_to_num('#')])\n",
    "        encoded_labels.append(np.pad(tmp, (0, max_len-tmp.shape[0]), constant_values=0))\n",
    "'''\n",
    "\n",
    "encoded_labels = np.load('../dataset/cv/train/y/encoded_labels_2.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Si inserisce alla fine di ogni array label la lunghezza reale della parola\n",
    "t_dist_dim = int(128)          # Questo valore indica quanti step temporali ci sono: nelle ultime feature map ci sono 128/4\n",
    "                                 # step temporali perché 128 è la larghezza massima tra le immagini in ingresso e 4 è il fattore\n",
    "                                 # di riduzione dovuto ai MaxPooling (ci sono 2 livelli di MaxPooling che dimezzano le dimensioni)\n",
    "enc2 = []\n",
    "for i in range(encoded_labels.shape[0]):\n",
    "    if len(labels[i]) == 0:\n",
    "        enc2.append(np.append(encoded_labels[i], [1, t_dist_dim]))\n",
    "    else:\n",
    "        enc2.append(np.append(encoded_labels[i], [len(labels[i]), t_dist_dim]))\n",
    "enc2 = np.array(enc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#with strategy.scope():\n",
    "net = build_and_compile_model_v10_2(input_shape=(128, 32, 1), len_characters=len(characters), opt=\"Adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 128, 32, 1)] 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 128, 32, 8)   80          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 128, 32, 8)   0           conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 128, 32, 16)  1168        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 128, 32, 16)  0           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 128, 32, 32)  4640        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 128, 32, 32)  0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 128, 32, 32)  128         activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 128, 32, 64)  18496       batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 128, 32, 64)  0           conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 128, 32, 64)  256         activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 128, 32, 120) 0           activation[0][0]                 \n",
      "                                                                 activation_1[0][0]               \n",
      "                                                                 batch_normalization[0][0]        \n",
      "                                                                 batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "time_distributed (TimeDistribut (None, 128, 3840)    0           concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 128, 128)     491648      time_distributed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional (Bidirectional)   (None, 128, 128)     98816       dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_1 (Bidirectional) (None, 128, 128)     98816       bidirectional[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 128, 63)      8127        bidirectional_1[0][0]            \n",
      "==================================================================================================\n",
      "Total params: 722,175\n",
      "Trainable params: 721,983\n",
      "Non-trainable params: 192\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "net.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
    "\n",
    "Early_Stopping_Patience = 10 \n",
    "Min_Delta = 0.0001\n",
    "# Model Check Point\n",
    "Check_Point = ModelCheckpoint( 'weights/CRNN_v10_2.h5',   # Filepath\n",
    "                               monitor='val_loss',\n",
    "                               save_best_only=True,\n",
    "                               verbose=1,\n",
    "                               mode='auto',\n",
    "                               save_weights_only=False,\n",
    "                               save_freq='epoch' )\n",
    "# Add early stopping\n",
    "Early_Stopping = EarlyStopping( monitor='val_loss',\n",
    "                                min_delta=Min_Delta,\n",
    "                                patience=Early_Stopping_Patience,\n",
    "                                verbose=1,\n",
    "                                mode='auto',\n",
    "                                baseline=None,\n",
    "                                restore_best_weights=True )\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,\n",
    "                              patience=10, min_lr=0.0002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "x_train = imgs\n",
    "y_train = enc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 37.7502\n",
      "Epoch 00001: val_loss improved from inf to 27.24130, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 9s 76ms/step - loss: 37.7502 - val_loss: 27.2413\n",
      "Epoch 2/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 26.4739\n",
      "Epoch 00002: val_loss improved from 27.24130 to 27.12935, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 26.4739 - val_loss: 27.1293\n",
      "Epoch 3/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 26.3062\n",
      "Epoch 00003: val_loss improved from 27.12935 to 26.59156, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 26.3062 - val_loss: 26.5916\n",
      "Epoch 4/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 26.2145\n",
      "Epoch 00004: val_loss improved from 26.59156 to 26.41762, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 26.2145 - val_loss: 26.4176\n",
      "Epoch 5/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 26.1694\n",
      "Epoch 00005: val_loss improved from 26.41762 to 26.35491, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 26.1694 - val_loss: 26.3549\n",
      "Epoch 6/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 26.0708\n",
      "Epoch 00006: val_loss improved from 26.35491 to 26.34517, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 26.0708 - val_loss: 26.3452\n",
      "Epoch 7/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 25.9677\n",
      "Epoch 00007: val_loss improved from 26.34517 to 26.07801, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 25.9677 - val_loss: 26.0780\n",
      "Epoch 8/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 25.6121\n",
      "Epoch 00008: val_loss improved from 26.07801 to 25.69537, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 25.6121 - val_loss: 25.6954\n",
      "Epoch 9/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 24.9354\n",
      "Epoch 00009: val_loss did not improve from 25.69537\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 24.9354 - val_loss: 27.4581\n",
      "Epoch 10/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 23.7329\n",
      "Epoch 00010: val_loss did not improve from 25.69537\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 23.7329 - val_loss: 27.6950\n",
      "Epoch 11/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 18.3304\n",
      "Epoch 00011: val_loss improved from 25.69537 to 23.10649, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 18.3304 - val_loss: 23.1065\n",
      "Epoch 12/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 10.6634\n",
      "Epoch 00012: val_loss improved from 23.10649 to 18.97560, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 9s 68ms/step - loss: 10.6634 - val_loss: 18.9756\n",
      "Epoch 13/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 6.2765\n",
      "Epoch 00013: val_loss improved from 18.97560 to 12.21751, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 6.2765 - val_loss: 12.2175\n",
      "Epoch 14/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 4.2736\n",
      "Epoch 00014: val_loss improved from 12.21751 to 10.68306, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 4.2736 - val_loss: 10.6831\n",
      "Epoch 15/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 2.9254\n",
      "Epoch 00015: val_loss improved from 10.68306 to 3.78184, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 2.9254 - val_loss: 3.7818\n",
      "Epoch 16/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 2.2964\n",
      "Epoch 00016: val_loss improved from 3.78184 to 2.56711, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 2.2964 - val_loss: 2.5671\n",
      "Epoch 17/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 1.7815\n",
      "Epoch 00017: val_loss improved from 2.56711 to 2.14634, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 1.7815 - val_loss: 2.1463\n",
      "Epoch 18/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 1.7492\n",
      "Epoch 00018: val_loss improved from 2.14634 to 2.10156, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 9s 68ms/step - loss: 1.7492 - val_loss: 2.1016\n",
      "Epoch 19/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 1.7022\n",
      "Epoch 00019: val_loss did not improve from 2.10156\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 1.7022 - val_loss: 3.5346\n",
      "Epoch 20/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 1.1649\n",
      "Epoch 00020: val_loss improved from 2.10156 to 1.82394, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 1.1649 - val_loss: 1.8239\n",
      "Epoch 21/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.9473\n",
      "Epoch 00021: val_loss improved from 1.82394 to 1.44203, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 0.9473 - val_loss: 1.4420\n",
      "Epoch 22/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.8488\n",
      "Epoch 00022: val_loss did not improve from 1.44203\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.8488 - val_loss: 5.3206\n",
      "Epoch 23/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 1.1282\n",
      "Epoch 00023: val_loss did not improve from 1.44203\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 1.1282 - val_loss: 7.6514\n",
      "Epoch 24/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 1.1290\n",
      "Epoch 00024: val_loss did not improve from 1.44203\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 1.1290 - val_loss: 6.6231\n",
      "Epoch 25/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.7162\n",
      "Epoch 00025: val_loss improved from 1.44203 to 1.35447, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 0.7162 - val_loss: 1.3545\n",
      "Epoch 26/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.5780\n",
      "Epoch 00026: val_loss improved from 1.35447 to 1.12608, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 9s 68ms/step - loss: 0.5780 - val_loss: 1.1261\n",
      "Epoch 27/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.5282\n",
      "Epoch 00027: val_loss improved from 1.12608 to 1.08636, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 0.5282 - val_loss: 1.0864\n",
      "Epoch 28/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.4866\n",
      "Epoch 00028: val_loss improved from 1.08636 to 1.07647, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 0.4866 - val_loss: 1.0765\n",
      "Epoch 29/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.4435\n",
      "Epoch 00029: val_loss did not improve from 1.07647\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.4435 - val_loss: 1.0957\n",
      "Epoch 30/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.4071\n",
      "Epoch 00030: val_loss did not improve from 1.07647\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.4071 - val_loss: 1.1312\n",
      "Epoch 31/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.3978\n",
      "Epoch 00031: val_loss improved from 1.07647 to 1.03900, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 0.3978 - val_loss: 1.0390\n",
      "Epoch 32/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.3473\n",
      "Epoch 00032: val_loss did not improve from 1.03900\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.3473 - val_loss: 1.3933\n",
      "Epoch 33/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.3731\n",
      "Epoch 00033: val_loss did not improve from 1.03900\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.3731 - val_loss: 1.4152\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.3704\n",
      "Epoch 00034: val_loss did not improve from 1.03900\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.3704 - val_loss: 7.3490\n",
      "Epoch 35/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.4229\n",
      "Epoch 00035: val_loss did not improve from 1.03900\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.4229 - val_loss: 1.2698\n",
      "Epoch 36/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.3658\n",
      "Epoch 00036: val_loss did not improve from 1.03900\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.3658 - val_loss: 1.7658\n",
      "Epoch 37/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.2876\n",
      "Epoch 00037: val_loss did not improve from 1.03900\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.2876 - val_loss: 1.7918\n",
      "Epoch 38/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.2849\n",
      "Epoch 00038: val_loss did not improve from 1.03900\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.2849 - val_loss: 5.5979\n",
      "Epoch 39/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.2709\n",
      "Epoch 00039: val_loss did not improve from 1.03900\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.2709 - val_loss: 1.2177\n",
      "Epoch 40/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.2329\n",
      "Epoch 00040: val_loss did not improve from 1.03900\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.2329 - val_loss: 1.1123\n",
      "Epoch 41/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.2338\n",
      "Epoch 00041: val_loss did not improve from 1.03900\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.2338 - val_loss: 1.1783\n",
      "Epoch 42/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.1448\n",
      "Epoch 00042: val_loss improved from 1.03900 to 0.97142, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 0.1448 - val_loss: 0.9714\n",
      "Epoch 43/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.1064\n",
      "Epoch 00043: val_loss improved from 0.97142 to 0.96743, saving model to weights/CRNN_v10_2.h5\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 0.1064 - val_loss: 0.9674\n",
      "Epoch 44/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0941\n",
      "Epoch 00044: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.0941 - val_loss: 0.9815\n",
      "Epoch 45/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0855\n",
      "Epoch 00045: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.0855 - val_loss: 0.9859\n",
      "Epoch 46/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0790\n",
      "Epoch 00046: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.0790 - val_loss: 0.9909\n",
      "Epoch 47/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0736\n",
      "Epoch 00047: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.0736 - val_loss: 0.9987\n",
      "Epoch 48/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0695\n",
      "Epoch 00048: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 0.0695 - val_loss: 1.0130\n",
      "Epoch 49/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0649\n",
      "Epoch 00049: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.0649 - val_loss: 1.0113\n",
      "Epoch 50/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0616\n",
      "Epoch 00050: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 0.0616 - val_loss: 1.0167\n",
      "Epoch 51/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0583\n",
      "Epoch 00051: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.0583 - val_loss: 1.0282\n",
      "Epoch 52/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0540\n",
      "Epoch 00052: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 0.0540 - val_loss: 1.0354\n",
      "Epoch 53/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0522\n",
      "Epoch 00053: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.0522 - val_loss: 1.0407\n",
      "Epoch 54/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0487\n",
      "Epoch 00054: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.0487 - val_loss: 1.0437\n",
      "Epoch 55/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0454\n",
      "Epoch 00055: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.0454 - val_loss: 1.0478\n",
      "Epoch 56/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0452\n",
      "Epoch 00056: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.0452 - val_loss: 1.0510\n",
      "Epoch 57/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0417\n",
      "Epoch 00057: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.0417 - val_loss: 1.0720\n",
      "Epoch 58/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0395\n",
      "Epoch 00058: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 67ms/step - loss: 0.0395 - val_loss: 1.0671\n",
      "Epoch 59/100\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0367\n",
      "Epoch 00059: val_loss did not improve from 0.96743\n",
      "125/125 [==============================] - 8s 68ms/step - loss: 0.0367 - val_loss: 1.0703\n",
      "Epoch 60/100\n",
      "109/125 [=========================>....] - ETA: 0s - loss: 0.0340"
     ]
    },
    {
     "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-10-9e46d52032c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mCheck_Point\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce_lr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    106\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1096\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m   1097\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\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   1099\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\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~/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    778\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 780\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\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[0mkwds\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    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\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~/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    805\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    806\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 807\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\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[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    808\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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    809\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2828\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2829\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2830\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2831\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1841\u001b[0m       \u001b[0;31m`\u001b[0m\u001b[0margs\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1842\u001b[0m     \"\"\"\n\u001b[0;32m-> 1843\u001b[0;31m     return self._call_flat(\n\u001b[0m\u001b[1;32m   1844\u001b[0m         [t for t in nest.flatten((args, kwargs), expand_composites=True)\n\u001b[1;32m   1845\u001b[0m          if isinstance(t, (ops.Tensor,\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1921\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1922\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1923\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1924\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1925\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    543\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\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[0;32m--> 545\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    546\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\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    547\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     57\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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---> 59\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "history = net.fit(x_train, y_train, validation_split=0.2, epochs=100, callbacks=[Check_Point, reduce_lr], batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('history/my_history_v5.npy',history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "his = np.load('my_history_v5.npy', allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f959cd6ce50>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(range(1, 101), history.history['loss'])\n",
    "plt.plot(range(1, 101), history.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imageio\n",
    "\n",
    "im = imageio.imread('../dataset/cv/train/mario.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-15-d15eb699efec>:5: FutureWarning: Non RGB image conversion is now deprecated. For RGBA images, please use rgb2gray(rgba2rgb(rgb)) instead. In version 0.19, a ValueError will be raised if input image last dimension length is not 3.\n",
      "  im = color.rgb2gray(im)\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "from skimage import color\n",
    "\n",
    "im = cv2.resize(im, (128, 128))\n",
    "im = color.rgb2gray(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = np.expand_dims(im, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_ctc(args):\n",
    "    \"\"\"returns a list of decoded ctc losses\"\"\"\n",
    "\n",
    "    y_pred, input_length = args\n",
    "\n",
    "    ctc_decoded = tf.keras.backend.ctc_decode(\n",
    "        y_pred, input_length, greedy=True)\n",
    "\n",
    "    return ctc_decoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/grauso/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:201: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n"
     ]
    }
   ],
   "source": [
    "pred_tensor, _ = decode_ctc([net.predict(np.expand_dims(im, axis=0)), np.array([t_dist_dim])])\n",
    "pred_labels = tf.keras.backend.get_value(pred_tensor[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 128), dtype=string, numpy=\n",
       "array([[b'E', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]']], dtype=object)>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_to_char(pred_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(im, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.load_weights('weights/CRNN_v10_5.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing modules for handwritten text generation.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=128x32 at 0x7F9DF34F76A0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from trdg.generators import GeneratorFromRandom  \n",
    "base_generator = GeneratorFromRandom(use_symbols=False, use_letters=True, length=1, width=128, size=32)\n",
    "img, lbl = next(base_generator)\n",
    "display(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from skimage import color\n",
    "im = cv2.resize(np.array(img), (128, 128))\n",
    "im = color.rgb2gray(im)\n",
    "im = np.expand_dims(im, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "import cv2\n",
    "from skimage import color\n",
    "import numpy as np\n",
    "imgs = []\n",
    "lbls = []\n",
    "for i in range(10000):\n",
    "    print(i)\n",
    "    img, lbl = next(base_generator)\n",
    "    im = color.rgb2gray(np.array(img))\n",
    "    im = np.expand_dims(im, axis=-1)\n",
    "    imgs.append(im)\n",
    "    lbls.append(lbl)\n",
    "np.savez_compressed('../dataset/cv/train/x/generated_text_2',np.array(imgs))\n",
    "np.save('../dataset/cv/train/y/generated_labels_2', np.array(lbls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2z3D'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lbls[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_tensor, _ = decode_ctc([net.predict(np.expand_dims(im, axis=0)), np.array([t_dist_dim])])\n",
    "pred_labels = tf.keras.backend.get_value(pred_tensor[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14, 25, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 128), dtype=string, numpy=\n",
       "array([[b'E', b'P', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]', b'[UNK]',\n",
       "        b'[UNK]']], dtype=object)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_to_char(pred_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ht'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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