graficar.ipynb

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   "metadata": {},
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   "source": [
    "%matplotlib inline\n",
    "import plotly.graph_objects as go\n",
    "import pickle\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [],
   "source": [
    "list_models=[\n",
    "    'model_baseline_v1.hdf5',\n",
    "    'model_v1.hdf5',\n",
    "    'model_baseline_dropout.h5',\n",
    "    'model_dropout.hdf5',\n",
    "    'model_baseline_tf_learning.h5',\n",
    "    'model_tf_learning_mobilnet.hdf5',\n",
    "    'model_tf_learning_ResNet50.hdf5',\n",
    "    'model_tf_learning_InceptionV3.hdf5',\n",
    "    'model_tf_learning_VGG16.hdf5'\n",
    "]\n",
    "x_models = [\n",
    "    'baseline_v1',\n",
    "    'my_v1',\n",
    "    'baseline_dropout',\n",
    "    'my_dropout',\n",
    "    'baseline_tf_learning',\n",
    "    'my_tf_learning_mobilnet',\n",
    "    'my_tf_learning_ResNet50',\n",
    "    'my_tf_learning_InceptionV3',\n",
    "    'my_tf_learning_VGG16'\n",
    "]\n",
    "\n",
    "with open('metric_models.pickle', 'rb') as f:\n",
    "    Models = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [],
   "source": [
    "Acc = []\n",
    "F1 = np.array([])\n",
    "for name_model in list_models:\n",
    "    Acc.append(Models[name_model]['val_acc'])\n",
    "    if F1.size == 0:\n",
    "        F1 = Models[name_model]['f1']\n",
    "    else:\n",
    "        F1 = np.vstack((F1, Models[name_model]['f1']))"
   ]
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    "F1"
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    "fig = go.Figure(\n",
    "    data=[go.Bar(y=Acc,x=x_models)],\n",
    "    layout_title_text=\"Acierto en validación\"\n",
    ")\n",
    "fig.show()"
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   "outputs": [
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     "data": {
      "text/plain": "(2, 7)"
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   ],
   "source": [
    "import numpy as np\n",
    "x = Models[list_models[0]]['f1']\n",
    "np.vstack((x,x)).shape"
   ]
  },
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    "labels = ['angry','disgust','fear','happy','neutral','sad','surprise']\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='baseline_v1', y=labels, x=F1[0,:],width=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],orientation='h',marker_color='rgb(255, 102, 102)'),\n",
    "    go.Bar(name='my_v1', y=labels, x=F1[1,:],width=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],orientation='h',marker_color='rgb(255, 255, 102)'),\n",
    "    go.Bar(name='baseline_dropout', y=labels, x=F1[2,:],width=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],orientation='h',marker_color='rgb(102, 255, 102)'),\n",
    "    go.Bar(name='my_dropout', y=labels, x=F1[3,:],width=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],orientation='h',marker_color='rgb(102, 255, 255)'),\n",
    "    go.Bar(name='baseline_tf_learning', y=labels, x=F1[4,:],width=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],orientation='h',marker_color='rgb(102, 102, 255)'),\n",
    "    go.Bar(name='my_tf_learning', y=labels, x=F1[5,:],width=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],orientation='h',marker_color='rgb(192, 192, 192)')],\n",
    "    layout_title_text=\"F1 Score\"\n",
    "    )\n",
    "# Change the bar mode\n",
    "fig.update_layout(barmode='group')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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   "source": [
    "labels = ['angry','disgust','fear','happy','neutral','sad','surprise']\n",
    "\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='dropout', y=labels, x=F1[3,:],width=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],orientation='h',marker_color='rgb(255, 102, 102)'),\n",
    "    go.Bar(name='baseline', y=labels, x=F1[4,:],width=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],orientation='h',marker_color='rgb(255, 255, 102)'),\n",
    "    go.Bar(name='mobilnet', y=labels, x=F1[5,:],width=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],orientation='h',marker_color='rgb(102, 255, 102)'),\n",
    "    go.Bar(name='ResNet50', y=labels, x=F1[6,:],width=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],orientation='h',marker_color='rgb(102, 255, 255)'),\n",
    "    go.Bar(name='InceptionV3', y=labels, x=F1[7,:],width=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],orientation='h',marker_color='rgb(102, 102, 255)'),\n",
    "    go.Bar(name='VGG16', y=labels, x=F1[8,:],width=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],orientation='h',marker_color='rgb(192, 192, 192)')],\n",
    "    layout_title_text=\"F1 Score\"\n",
    "    )\n",
    "# Change the bar mode\n",
    "fig.update_layout(barmode='group')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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