{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "executionInfo": {
     "elapsed": 24689,
     "status": "ok",
     "timestamp": 1744101251163,
     "user": {
      "displayName": "Dinesh Kumar",
      "userId": "18299454607260962281"
     },
     "user_tz": -330
    },
    "id": "Ni_Q3LdXWC-q",
    "outputId": "5f3fff46-29a3-41ac-c79f-9ca052214953"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'google.colab'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Mount Google Drive\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgoogle\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcolab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m drive\n\u001b[0;32m      3\u001b[0m drive\u001b[38;5;241m.\u001b[39mmount(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/content/drive\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'google.colab'"
     ]
    }
   ],
   "source": [
    "# Mount Google Drive\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n",
    "import pandas as pd\n",
    "\n",
    "# Load the match + commentary data\n",
    "csv_path = '/content/drive/MyDrive/Colab Notebooks/IPLPrediction/gru_match_simulation_commentary.csv'\n",
    "df = pd.read_csv(csv_path)\n",
    "\n",
    "# Add DELTA columns for comparison between overs\n",
    "df['Runs_This_Over'] = df['Cumulative Runs'].diff().fillna(df['Cumulative Runs'])\n",
    "df['Score_Delta'] = df['Predicted Final Score'].diff().fillna(df['Predicted Final Score'])\n",
    "df['Win_Prob_Change'] = df['Win Probability (%)'].diff().fillna(df['Win Probability (%)'])\n",
    "\n",
    "# Display result\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 57,
     "status": "ok",
     "timestamp": 1744099400163,
     "user": {
      "displayName": "Dinesh Kumar",
      "userId": "18299454607260962281"
     },
     "user_tz": -330
    },
    "id": "oizD-WjjbM1V",
    "outputId": "d6303264-2d78-4b3a-a7d5-2cf2d5c5ec35"
   },
   "outputs": [],
   "source": [
    "# 💥 Highest run overs\n",
    "top_run_overs = df.sort_values(by='Runs_This_Over', ascending=False).head(3)\n",
    "\n",
    "# 🎯 Biggest predicted score jump\n",
    "top_score_jump = df.sort_values(by='Score_Delta', ascending=False).head(3)\n",
    "\n",
    "# 🔻 Biggest drop in win probability\n",
    "biggest_win_prob_drop = df.sort_values(by='Win_Prob_Change').head(3)\n",
    "\n",
    "# 🔼 Biggest increase in win probability\n",
    "biggest_win_prob_rise = df.sort_values(by='Win_Prob_Change', ascending=False).head(3)\n",
    "\n",
    "# Display each\n",
    "print(\"💥 Top 3 High-Scoring Overs:\")\n",
    "print(top_run_overs[['Over', 'Runs_This_Over', 'Commentary']], end=\"\\n\\n\")\n",
    "\n",
    "print(\"🎯 Top 3 Predicted Score Jumps:\")\n",
    "print(top_score_jump[['Over', 'Score_Delta', 'Commentary']], end=\"\\n\\n\")\n",
    "\n",
    "print(\"🔻 Top 3 Win Probability Drops:\")\n",
    "print(biggest_win_prob_drop[['Over', 'Win_Prob_Change', 'Commentary']], end=\"\\n\\n\")\n",
    "\n",
    "print(\"🔼 Top 3 Win Probability Gains:\")\n",
    "print(biggest_win_prob_rise[['Over', 'Win_Prob_Change', 'Commentary']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5058,
     "status": "ok",
     "timestamp": 1744101274146,
     "user": {
      "displayName": "Dinesh Kumar",
      "userId": "18299454607260962281"
     },
     "user_tz": -330
    },
    "id": "7zlfOX9pbMzU",
    "outputId": "5e8a9048-60cf-43a4-c8e8-a3c712da238a"
   },
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "import os\n",
    "\n",
    "# Set your API key securely\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"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\"  # 🔐 Use your actual key here\n",
    "client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n",
    "\n",
    "# Construct the summary prompt from insights\n",
    "summary_prompt = \"\"\"\n",
    "You're a cricket commentator. Generate an IPL-style summary of the match based on these insights:\n",
    "\n",
    "- The first 3 overs saw very little movement (low runs, 0% win probability).\n",
    "- Over 5 to Over 7 showed good progress — higher runs and confidence.\n",
    "- Over 19 was a massive momentum swing — highest runs, max predicted score jump, and win probability jump (22%).\n",
    "- Over 20 ended with a predicted score of 154 and a win probability of 43%.\n",
    "\n",
    "Write the commentary in a high-energy, sharp, and story-like tone. Mention momentum shift, turning points, and crowd reactions.\n",
    "\"\"\"\n",
    "\n",
    "# Get GPT response\n",
    "# Re-run GPT summary generation with more tokens\n",
    "response = client.chat.completions.create(\n",
    "    model=\"gpt-3.5-turbo\",\n",
    "    messages=[{\"role\": \"user\", \"content\": summary_prompt}],\n",
    "    temperature=0.9,\n",
    "    max_tokens=350  # ✅ Increased to avoid truncation\n",
    ")\n",
    "\n",
    "match_summary = response.choices[0].message.content\n",
    "print(\"🏏 Full Match Summary:\\n\")\n",
    "print(match_summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 284,
     "status": "ok",
     "timestamp": 1744101284093,
     "user": {
      "displayName": "Dinesh Kumar",
      "userId": "18299454607260962281"
     },
     "user_tz": -330
    },
    "id": "-M-DEfnCbMwv",
    "outputId": "01a1ef6c-cdcd-464f-d557-a1b2526704de"
   },
   "outputs": [],
   "source": [
    "# Save as text file\n",
    "summary_path_txt = '/content/drive/MyDrive/Colab Notebooks/IPLPrediction/gru_match_summary.txt'\n",
    "\n",
    "with open(summary_path_txt, 'w') as f:\n",
    "    f.write(match_summary)\n",
    "\n",
    "print(f\"✅ Full match summary saved to: {summary_path_txt}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 734
    },
    "executionInfo": {
     "elapsed": 1927,
     "status": "ok",
     "timestamp": 1744101288885,
     "user": {
      "displayName": "Dinesh Kumar",
      "userId": "18299454607260962281"
     },
     "user_tz": -330
    },
    "id": "NoH11T4obMnf",
    "outputId": "26391ad0-a59a-48c1-b4a9-e48226753c26"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load commentary data (already saved earlier)\n",
    "file_path = '/content/drive/MyDrive/Colab Notebooks/IPLPrediction/gru_match_simulation_commentary.csv'\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Prepare values\n",
    "overs = df['Over']\n",
    "runs_this_over = df['Cumulative Runs'].diff().fillna(df['Cumulative Runs'])\n",
    "win_prob = df['Win Probability (%)']\n",
    "\n",
    "# Create plot\n",
    "fig, ax1 = plt.subplots(figsize=(14, 6))\n",
    "\n",
    "# 🟦 Bar: Runs per over\n",
    "bars = ax1.bar(overs, runs_this_over, color='dodgerblue', label='Runs This Over')\n",
    "ax1.set_xlabel(\"Over\", fontsize=12)\n",
    "ax1.set_ylabel(\"Runs Scored\", color='dodgerblue', fontsize=12)\n",
    "ax1.tick_params(axis='y', labelcolor='dodgerblue')\n",
    "ax1.set_xticks(range(1, 21))\n",
    "\n",
    "# 🟧 Line: Win Probability %\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(overs, win_prob, color='orange', linewidth=2.5, label='Win Probability (%)')\n",
    "ax2.set_ylabel(\"Win Probability (%)\", color='orange', fontsize=12)\n",
    "ax2.tick_params(axis='y', labelcolor='orange')\n",
    "\n",
    "# 🎯 Title and Layout\n",
    "plt.title(\"🏏 Match Momentum Dashboard – Runs vs Win Probability\", fontsize=15, fontweight='bold')\n",
    "fig.tight_layout()\n",
    "\n",
    "# Save output\n",
    "momentum_plot_path = \"/content/drive/MyDrive/Colab Notebooks/IPLPrediction/match_momentum_dashboard.png\"\n",
    "plt.savefig(momentum_plot_path, dpi=300)\n",
    "plt.show()\n",
    "\n",
    "print(f\"✅ Momentum dashboard saved to: {momentum_plot_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6907,
     "status": "ok",
     "timestamp": 1744101439165,
     "user": {
      "displayName": "Dinesh Kumar",
      "userId": "18299454607260962281"
     },
     "user_tz": -330
    },
    "id": "GoZr1Knoi3sf",
    "outputId": "f7774c23-585d-4ac2-808c-da79fbc52ab4"
   },
   "outputs": [],
   "source": [
    "!pip install streamlit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5762,
     "status": "ok",
     "timestamp": 1744101743444,
     "user": {
      "displayName": "Dinesh Kumar",
      "userId": "18299454607260962281"
     },
     "user_tz": -330
    },
    "id": "Ew5xg6xxizSM",
    "outputId": "9655ba3b-e7b5-49a4-b5ea-a4580ad8c081"
   },
   "outputs": [],
   "source": [
    "!pip uninstall matplotlib -y\n",
    "!pip install matplotlib --upgrade --force-reinstall\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install scipy==1.13.0 scikit-learn==1.4.1.post1 pillow==10.2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import joblib\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "def simulate_gru_final_score(current_runs, current_overs, wickets, gru_model_path, run_scaler_path, score_scaler_path):\n",
    "    \"\"\"\n",
    "    Simulate final score from current match state using trained GRU model.\n",
    "\n",
    "    Parameters:\n",
    "    - current_runs: list of cumulative runs till current over (length = current_overs)\n",
    "    - current_overs: int (number of completed overs)\n",
    "    - wickets: int (optional, not used in prediction directly)\n",
    "    - gru_model_path: path to trained GRU .keras model\n",
    "    - run_scaler_path: fitted MinMaxScaler for input sequence (joblib path)\n",
    "    - score_scaler_path: fitted MinMaxScaler for output (joblib path)\n",
    "\n",
    "    Returns:\n",
    "    - predicted_final_score (float)\n",
    "    \"\"\"\n",
    "    # Load scalers\n",
    "    input_scaler = joblib.load(run_scaler_path)\n",
    "    output_scaler = joblib.load(score_scaler_path)\n",
    "\n",
    "    # Load GRU model\n",
    "    model = load_model(gru_model_path, compile=False)\n",
    "\n",
    "    # Pad sequence to 20 overs\n",
    "    seq = np.array(current_runs).reshape(-1, 1)\n",
    "    padded_seq = np.pad(seq, ((0, 20 - len(seq)), (0, 0)), mode='constant')\n",
    "\n",
    "    # Scale input\n",
    "    scaled_input = input_scaler.transform(padded_seq).reshape(1, 20, 1)\n",
    "\n",
    "    # Predict\n",
    "    pred_scaled = model.predict(scaled_input, verbose=0)\n",
    "    predicted_final_score = output_scaler.inverse_transform(pred_scaled)[0][0]\n",
    "\n",
    "    return round(predicted_final_score, 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "# Load the trained .h5 model\n",
    "model = load_model(\"gru_score_predictor.h5\", compile=False)\n",
    "\n",
    "# Save in .keras format\n",
    "model.save(\"gru_score_predictor.keras\", save_format=\"keras\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['scaler_output.save']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Simulate training data (same structure as used earlier)\n",
    "cumulative_runs = np.random.randint(0, 200, size=(400, 1))  # mimic overwise cumulative runs\n",
    "final_scores = np.random.randint(100, 220, size=(400, 1))   # mimic final score ranges\n",
    "\n",
    "# Create and fit scalers\n",
    "scaler_input = MinMaxScaler().fit(cumulative_runs)\n",
    "scaler_output = MinMaxScaler().fit(final_scores)\n",
    "\n",
    "# Save scalers for future use\n",
    "joblib.dump(scaler_input, 'scaler_input.save')\n",
    "joblib.dump(scaler_output, 'scaler_output.save')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dine24\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0034383272286504507"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-execute after environment reset\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import GRU, Dense\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import joblib\n",
    "import os\n",
    "\n",
    "# Step 1: Simulate sample training data (Runs + Wickets as input, Final Score as output)\n",
    "np.random.seed(42)\n",
    "\n",
    "# Generate 500 samples\n",
    "samples = 500\n",
    "overs = 20\n",
    "\n",
    "# Random per-over runs (0 to 20)\n",
    "runs_per_over = np.random.randint(0, 21, size=(samples, overs))\n",
    "\n",
    "# Random per-over wickets (0 to 2)\n",
    "wickets_per_over = np.random.randint(0, 3, size=(samples, overs))\n",
    "\n",
    "# Cumulative input: concatenate cumulative runs and cumulative wickets\n",
    "cumulative_runs = np.cumsum(runs_per_over, axis=1)\n",
    "cumulative_wickets = np.cumsum(wickets_per_over, axis=1)\n",
    "\n",
    "# Combine features\n",
    "X_combined = np.stack((cumulative_runs, cumulative_wickets), axis=2)  # shape = (samples, 20, 2)\n",
    "\n",
    "# Generate final scores (simulate realistic final score with some noise)\n",
    "final_scores = cumulative_runs[:, -1] + np.random.normal(loc=5.0, scale=10.0, size=(samples,))\n",
    "final_scores = final_scores.reshape(-1, 1)\n",
    "\n",
    "# Step 2: Normalize\n",
    "input_scaler = MinMaxScaler()\n",
    "output_scaler = MinMaxScaler()\n",
    "\n",
    "X_scaled = input_scaler.fit_transform(X_combined.reshape(-1, 2)).reshape(samples, overs, 2)\n",
    "y_scaled = output_scaler.fit_transform(final_scores)\n",
    "\n",
    "# Step 3: Build GRU model\n",
    "model = Sequential()\n",
    "model.add(GRU(64, input_shape=(20, 2), return_sequences=False))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "# Step 4: Train\n",
    "early_stop = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "history = model.fit(X_scaled, y_scaled, epochs=50, batch_size=32, validation_split=0.2, callbacks=[early_stop], verbose=0)\n",
    "\n",
    "# Step 5: Save model and scalers\n",
    "os.makedirs(\"trained_model\", exist_ok=True)\n",
    "model.save(\"trained_model/gru_runs_wickets.keras\")\n",
    "joblib.dump(input_scaler, \"trained_model/scaler_input_rw.save\")\n",
    "joblib.dump(output_scaler, \"trained_model/scaler_output_rw.save\")\n",
    "\n",
    "# Return final loss to confirm successful training\n",
    "final_loss = history.history['val_loss'][-1]\n",
    "final_loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ql4r1e5hkcG1",
    "outputId": "4c067f05-a996-4c2d-b265-3124b6ed99e1"
   },
   "outputs": [],
   "source": [
    "!streamlit run app.py\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!streamlit run match_simulator.py\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ace_tools in c:\\users\\dine24\\anaconda3\\lib\\site-packages (0.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution ~onttools (C:\\Users\\Dine24\\anaconda3\\Lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution ~onttools (C:\\Users\\Dine24\\anaconda3\\Lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution ~onttools (C:\\Users\\Dine24\\anaconda3\\Lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "!pip install ace_tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!streamlit run \"C:/Users/Dine24/Python Course/IPL_Cricket/trained_model/match_simulator_rw.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dine24\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "198.30263"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import GRU, Dense\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import joblib\n",
    "\n",
    "# Simulate training data (runs + wickets as input, final score as output)\n",
    "np.random.seed(42)\n",
    "num_samples = 500\n",
    "\n",
    "# Generate random over-wise data for 20 overs\n",
    "runs_data = np.random.randint(0, 21, size=(num_samples, 20))  # 0-20 runs per over\n",
    "wickets_data = np.random.binomial(1, 0.3, size=(num_samples, 20))  # 0 or 1 wickets per over\n",
    "\n",
    "# Input shape: [samples, 20 overs, 2 features]\n",
    "X = np.stack((runs_data, wickets_data), axis=2)\n",
    "\n",
    "# Output: final scores with some variation based on runs and wickets\n",
    "final_scores = runs_data.sum(axis=1) + np.random.normal(0, 5, size=num_samples) - (wickets_data.sum(axis=1) * 2)\n",
    "y = final_scores.reshape(-1, 1)\n",
    "\n",
    "# Normalize inputs and outputs\n",
    "scaler_input = MinMaxScaler()\n",
    "X_reshaped = X.reshape(-1, 2)\n",
    "X_scaled = scaler_input.fit_transform(X_reshaped).reshape(num_samples, 20, 2)\n",
    "\n",
    "scaler_output = MinMaxScaler()\n",
    "y_scaled = scaler_output.fit_transform(y)\n",
    "\n",
    "# Define GRU model\n",
    "model = Sequential([\n",
    "    GRU(64, input_shape=(20, 2), return_sequences=False),\n",
    "    Dense(1)\n",
    "])\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.fit(X_scaled, y_scaled, epochs=10, batch_size=32, verbose=0)\n",
    "\n",
    "# Save model and scalers\n",
    "model.save(\"match_live_predictor/gru_score_predictor_rw.keras\")\n",
    "joblib.dump(scaler_input, \"scaler_rw_input.save\")\n",
    "joblib.dump(scaler_output, \"scaler_rw_output.save\")\n",
    "\n",
    "# Predict on a new random sample for validation\n",
    "sample_index = 0\n",
    "sample_input = X_scaled[sample_index:sample_index+1]\n",
    "pred_scaled = model.predict(sample_input, verbose=0)\n",
    "predicted_score = scaler_output.inverse_transform(pred_scaled)[0][0]\n",
    "\n",
    "predicted_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Scalers saved successfully!\n"
     ]
    }
   ],
   "source": [
    "# scaler_preparation_rw.py\n",
    "import numpy as np\n",
    "import joblib\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Simulated dummy data for fitting scalers\n",
    "runs_input = np.random.randint(0, 220, size=(400, 2))  # 2 features: runs + wickets\n",
    "final_scores = np.random.randint(100, 250, size=(400, 1))\n",
    "\n",
    "# Create and fit scalers\n",
    "scaler_input_rw = MinMaxScaler().fit(runs_input)\n",
    "scaler_output_rw = MinMaxScaler().fit(final_scores)\n",
    "\n",
    "# Save scalers\n",
    "joblib.dump(scaler_input_rw, 'match_live_predictor/scaler_input_rw.save')\n",
    "joblib.dump(scaler_output_rw, 'match_live_predictor/scaler_output_rw.save')\n",
    "\n",
    "print(\"✅ Scalers saved successfully!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m40s\u001b[0m 1s/step - loss: 0.1794\n",
      "\u001b[1m12/32\u001b[0m \u001b[32m=======\u001b[0m\u001b[37m=============\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1057\n",
      "\u001b[1m24/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0790\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0690\n",
      "Epoch 2/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0300\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0237 \n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0231\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0226\n",
      "Epoch 3/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.0166\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0191 \n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0193\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0192\n",
      "Epoch 4/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.0129\n",
      "\u001b[1m14/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0187 \n",
      "\u001b[1m26/32\u001b[0m \u001b[32m================\u001b[0m\u001b[37m====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0177\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0173\n",
      "Epoch 5/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.0137\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0134 \n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0130\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0128\n",
      "Epoch 6/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.0071\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0091 \n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0089\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0088\n",
      "Epoch 7/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.0048\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0064 \n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0072\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0074\n",
      "Epoch 8/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.0090\n",
      "\u001b[1m12/32\u001b[0m \u001b[32m=======\u001b[0m\u001b[37m=============\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0091 \n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0093\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0094\n",
      "Epoch 9/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.0090\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0073 \n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0072\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0072\n",
      "Epoch 10/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.0089\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0069 \n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0070\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0071\n",
      "Epoch 11/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0095\n",
      "\u001b[1m14/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0139 \n",
      "\u001b[1m26/32\u001b[0m \u001b[32m================\u001b[0m\u001b[37m====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0128\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0123\n",
      "Epoch 12/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.0049\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0088 \n",
      "\u001b[1m26/32\u001b[0m \u001b[32m================\u001b[0m\u001b[37m====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0083\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0080\n",
      "Epoch 13/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.0098\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0074 \n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0070\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0069\n",
      "Epoch 14/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 0.0067\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0090\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0085\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0081\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0079\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0077\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0075\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0074\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.0074\n",
      "Epoch 15/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 0.0061\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0051\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0052\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0055\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0058\n",
      "\u001b[1m20/32\u001b[0m \u001b[32m============\u001b[0m\u001b[37m========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0059\n",
      "\u001b[1m24/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0061\n",
      "\u001b[1m28/32\u001b[0m \u001b[32m=================\u001b[0m\u001b[37m===\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0062\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0063\n",
      "Epoch 16/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 0.0059\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0061\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0062\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0062\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0064\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0064\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0064\n",
      "\u001b[1m30/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0065\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0065\n",
      "Epoch 17/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 61ms/step - loss: 0.0091\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0082\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0081\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0079\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0076\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0073\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0072\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0071\n",
      "Epoch 18/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0056\n",
      "\u001b[1m 6/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0070\n",
      "\u001b[1m10/32\u001b[0m \u001b[32m======\u001b[0m\u001b[37m==============\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0070\n",
      "\u001b[1m14/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0071\n",
      "\u001b[1m18/32\u001b[0m \u001b[32m===========\u001b[0m\u001b[37m=========\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0071\n",
      "\u001b[1m22/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0071\n",
      "\u001b[1m26/32\u001b[0m \u001b[32m================\u001b[0m\u001b[37m====\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0071\n",
      "\u001b[1m30/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0071\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0071\n",
      "Epoch 19/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 63ms/step - loss: 0.0039\n",
      "\u001b[1m 6/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0072\n",
      "\u001b[1m10/32\u001b[0m \u001b[32m======\u001b[0m\u001b[37m==============\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0076\n",
      "\u001b[1m14/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0076\n",
      "\u001b[1m18/32\u001b[0m \u001b[32m===========\u001b[0m\u001b[37m=========\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0075\n",
      "\u001b[1m22/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0075\n",
      "\u001b[1m26/32\u001b[0m \u001b[32m================\u001b[0m\u001b[37m====\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0074\n",
      "\u001b[1m30/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0073\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0072\n",
      "Epoch 20/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 45ms/step - loss: 0.0049\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0054\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0063\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0066\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0068\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0070\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0071\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0071\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0071\n",
      "Epoch 21/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 0.0041\n",
      "\u001b[1m 6/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0059\n",
      "\u001b[1m10/32\u001b[0m \u001b[32m======\u001b[0m\u001b[37m==============\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0060\n",
      "\u001b[1m14/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0061\n",
      "\u001b[1m18/32\u001b[0m \u001b[32m===========\u001b[0m\u001b[37m=========\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0061\n",
      "\u001b[1m22/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0060\n",
      "\u001b[1m26/32\u001b[0m \u001b[32m================\u001b[0m\u001b[37m====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0061\n",
      "\u001b[1m30/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0062\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0062\n",
      "Epoch 22/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0121\n",
      "\u001b[1m 6/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0076\n",
      "\u001b[1m10/32\u001b[0m \u001b[32m======\u001b[0m\u001b[37m==============\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0077\n",
      "\u001b[1m14/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0077\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0078\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0078\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0078\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0078\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0078\n",
      "Epoch 23/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 64ms/step - loss: 0.0083\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0076\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0072\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0071\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0073\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0073\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0073\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0073\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0073\n",
      "Epoch 24/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m2s\u001b[0m 84ms/step - loss: 0.0107\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0076\n",
      "\u001b[1m 8/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0074\n",
      "\u001b[1m12/32\u001b[0m \u001b[32m=======\u001b[0m\u001b[37m=============\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0072\n",
      "\u001b[1m16/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0071\n",
      "\u001b[1m20/32\u001b[0m \u001b[32m============\u001b[0m\u001b[37m========\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0070\n",
      "\u001b[1m24/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0070\n",
      "\u001b[1m28/32\u001b[0m \u001b[32m=================\u001b[0m\u001b[37m===\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0069\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0069\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0069\n",
      "Epoch 25/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.0113\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0110\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0105\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0100\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0096\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0093\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0090\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0087\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0085\n",
      "Epoch 26/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 0.0050\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0070\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0073\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0074\n",
      "\u001b[1m19/32\u001b[0m \u001b[32m===========\u001b[0m\u001b[37m=========\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0073\n",
      "\u001b[1m28/32\u001b[0m \u001b[32m=================\u001b[0m\u001b[37m===\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0072\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0071\n",
      "Epoch 27/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 0.0115\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0100\n",
      "\u001b[1m10/32\u001b[0m \u001b[32m======\u001b[0m\u001b[37m==============\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0087\n",
      "\u001b[1m16/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0081\n",
      "\u001b[1m22/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0078\n",
      "\u001b[1m28/32\u001b[0m \u001b[32m=================\u001b[0m\u001b[37m===\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0075\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0074\n",
      "Epoch 28/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 49ms/step - loss: 0.0046\n",
      "\u001b[1m 8/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0065 \n",
      "\u001b[1m14/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0065\n",
      "\u001b[1m23/32\u001b[0m \u001b[32m==============\u001b[0m\u001b[37m======\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0067\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0068\n",
      "Epoch 29/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 0.0080\n",
      "\u001b[1m 6/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0070\n",
      "\u001b[1m12/32\u001b[0m \u001b[32m=======\u001b[0m\u001b[37m=============\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0071\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0071\n",
      "\u001b[1m22/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0071\n",
      "\u001b[1m31/32\u001b[0m \u001b[32m===================\u001b[0m\u001b[37m=\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0071 \n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0071\n",
      "Epoch 30/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 44ms/step - loss: 0.0055\n",
      "\u001b[1m 6/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0059\n",
      "\u001b[1m11/32\u001b[0m \u001b[32m======\u001b[0m\u001b[37m==============\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0057\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0059\n",
      "\u001b[1m23/32\u001b[0m \u001b[32m==============\u001b[0m\u001b[37m======\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0059\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0060\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0061\n",
      "Epoch 31/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - loss: 0.0066\n",
      "\u001b[1m 6/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0070\n",
      "\u001b[1m12/32\u001b[0m \u001b[32m=======\u001b[0m\u001b[37m=============\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0076\n",
      "\u001b[1m18/32\u001b[0m \u001b[32m===========\u001b[0m\u001b[37m=========\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0074\n",
      "\u001b[1m22/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0073\n",
      "\u001b[1m26/32\u001b[0m \u001b[32m================\u001b[0m\u001b[37m====\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0072\n",
      "\u001b[1m30/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0071\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0071\n",
      "Epoch 32/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 64ms/step - loss: 0.0041\n",
      "\u001b[1m 6/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0055\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0061 \n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0062\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0063\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0063\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0064\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0064\n",
      "Epoch 33/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 0.0060\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0064\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0067\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0071\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0071\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0071\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0070\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0070\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0070\n",
      "Epoch 34/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 0.0038\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0057\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0064\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0064\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0065\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0065\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0066\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0066\n",
      "Epoch 35/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.0058\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0062\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0061\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0061\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0061\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0061\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0062\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0062\n",
      "Epoch 36/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.0124\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0090\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0083\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0080\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0078\n",
      "\u001b[1m20/32\u001b[0m \u001b[32m============\u001b[0m\u001b[37m========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0077\n",
      "\u001b[1m24/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0076\n",
      "\u001b[1m28/32\u001b[0m \u001b[32m=================\u001b[0m\u001b[37m===\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0075\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0074\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0074\n",
      "Epoch 37/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 0.0065\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0063\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0061\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0059\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0059\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0060\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0061\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0061\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.0062\n",
      "Epoch 38/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0079\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0067\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0067\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0066\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0065\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0064\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0064\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0063\n",
      "Epoch 39/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 46ms/step - loss: 0.0058\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0062\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0062\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0061\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0062\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0064\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0064\n",
      "Epoch 40/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 62ms/step - loss: 0.0093\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0088\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0077\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0074\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0072\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0071\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0071\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0071\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0070\n",
      "Epoch 41/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 62ms/step - loss: 0.0059\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0064\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0067\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0070\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0071\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0070\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0070\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0070\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0070\n",
      "Epoch 42/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 61ms/step - loss: 0.0047\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0055\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0057\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0058\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0060\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0061\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0062\n",
      "\u001b[1m30/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0063\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0064\n",
      "Epoch 43/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - loss: 0.0056\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0053\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0054\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0055\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0056\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0058\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0059\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0060\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0061\n",
      "Epoch 44/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 57ms/step - loss: 0.0050\n",
      "\u001b[1m 8/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0065 \n",
      "\u001b[1m15/32\u001b[0m \u001b[32m=========\u001b[0m\u001b[37m===========\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0069\n",
      "\u001b[1m22/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0070\n",
      "\u001b[1m31/32\u001b[0m \u001b[32m===================\u001b[0m\u001b[37m=\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0070\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0070\n",
      "Epoch 45/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 64ms/step - loss: 0.0067\n",
      "\u001b[1m 8/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0061 \n",
      "\u001b[1m18/32\u001b[0m \u001b[32m===========\u001b[0m\u001b[37m=========\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0062\n",
      "\u001b[1m27/32\u001b[0m \u001b[32m================\u001b[0m\u001b[37m====\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0063\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0063\n",
      "Epoch 46/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 0.0030\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0041\n",
      "\u001b[1m11/32\u001b[0m \u001b[32m======\u001b[0m\u001b[37m==============\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0045\n",
      "\u001b[1m16/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0047\n",
      "\u001b[1m20/32\u001b[0m \u001b[32m============\u001b[0m\u001b[37m========\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0050\n",
      "\u001b[1m24/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0052\n",
      "\u001b[1m28/32\u001b[0m \u001b[32m=================\u001b[0m\u001b[37m===\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0054\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0055\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0056\n",
      "Epoch 47/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 0.0048\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0062\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0069\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0072\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0074\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0075\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0076\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0077\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0077\n",
      "Epoch 48/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 0.0039\n",
      "\u001b[1m 5/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0064\n",
      "\u001b[1m 9/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0067\n",
      "\u001b[1m13/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0069\n",
      "\u001b[1m17/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0070\n",
      "\u001b[1m21/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0070\n",
      "\u001b[1m25/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0069\n",
      "\u001b[1m29/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0069\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0069\n",
      "Epoch 49/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0036\n",
      "\u001b[1m 8/32\u001b[0m \u001b[32m=====\u001b[0m\u001b[37m===============\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0058 \n",
      "\u001b[1m12/32\u001b[0m \u001b[32m=======\u001b[0m\u001b[37m=============\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0060\n",
      "\u001b[1m16/32\u001b[0m \u001b[32m==========\u001b[0m\u001b[37m==========\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0061\n",
      "\u001b[1m20/32\u001b[0m \u001b[32m============\u001b[0m\u001b[37m========\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0061\n",
      "\u001b[1m24/32\u001b[0m \u001b[32m===============\u001b[0m\u001b[37m=====\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0061\n",
      "\u001b[1m31/32\u001b[0m \u001b[32m===================\u001b[0m\u001b[37m=\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0062\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0063\n",
      "Epoch 50/50\n",
      "\n",
      "\u001b[1m 1/32\u001b[0m \u001b[37m====================\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0047\n",
      "\u001b[1m 6/32\u001b[0m \u001b[32m===\u001b[0m\u001b[37m=================\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0052\n",
      "\u001b[1m10/32\u001b[0m \u001b[32m======\u001b[0m\u001b[37m==============\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0054\n",
      "\u001b[1m14/32\u001b[0m \u001b[32m========\u001b[0m\u001b[37m============\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0056\n",
      "\u001b[1m18/32\u001b[0m \u001b[32m===========\u001b[0m\u001b[37m=========\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0057\n",
      "\u001b[1m22/32\u001b[0m \u001b[32m=============\u001b[0m\u001b[37m=======\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0058\n",
      "\u001b[1m26/32\u001b[0m \u001b[32m================\u001b[0m\u001b[37m====\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0059\n",
      "\u001b[1m30/32\u001b[0m \u001b[32m==================\u001b[0m\u001b[37m==\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0060\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m====================\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0061\n",
      "GRU model retrained and saved successfully!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-04-09 11:37:28.632025: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2025-04-09 11:37:29.328822: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2025-04-09 11:37:31.986338: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "!python \"C:/Users/Dine24/Python Course/IPL_Cricket/match_live_predictor/train_gru_rw_model.py\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!streamlit run \"C:/Users/Dine24/Python Course/IPL_Cricket/match_live_predictor/live_predictor.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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