diff --git a/ModelPerformance.py b/ModelPerformance.py
index e8cb33c..8f1b57d 100644
--- a/ModelPerformance.py
+++ b/ModelPerformance.py
@@ -1,6 +1,15 @@
 import numpy as np
 import  matplotlib.pyplot as plt
 from sklearn.metrics import mean_squared_error
+def std_inverse(std,data):
+    original_shape = data.shape
+    try:
+        data = data.copy()
+    except Exception:
+        pass
+    inversed_data = std.inverse_transform(np.reshape(data,(-1,data.shape[-2]*data.shape[-1])))
+    inversed_data = inversed_data.reshape(original_shape)
+    return inversed_data
 def compare_result(predict,real):
     assert(predict.shape == real.shape)
     original_shape = predict.shape
@@ -27,12 +36,3 @@ def plot_result(predict,real,batch =0,in_out = 0):
     axs[2].imshow(np.abs(predict[batch,in_out,:,:] - real[batch,in_out,:,:]), cmap = cmap,vmin = vmin,vmax = vmax)
 
     plt.show()
-def std_inverse(std,data):
-    original_shape = data.shape
-    try:
-        data = data.copy()
-    except Exception:
-        pass
-    inversed_data = std.inverse_transform(np.reshape(data,(-1,data.shape[-2]*data.shape[-1])))
-    inversed_data = inversed_data.reshape(original_shape)
-    return inversed_data
\ No newline at end of file
diff --git a/__pycache__/GenDs.cpython-311.pyc b/__pycache__/GenDs.cpython-311.pyc
index b51d304..14b86bd 100644
Binary files a/__pycache__/GenDs.cpython-311.pyc and b/__pycache__/GenDs.cpython-311.pyc differ
diff --git a/__pycache__/ModelPerformance.cpython-311.pyc b/__pycache__/ModelPerformance.cpython-311.pyc
index a67d61b..c726795 100644
Binary files a/__pycache__/ModelPerformance.cpython-311.pyc and b/__pycache__/ModelPerformance.cpython-311.pyc differ
diff --git a/base.h5 b/base.h5
index af5a4bf..814f01a 100644
Binary files a/base.h5 and b/base.h5 differ
diff --git a/model_lab.ipynb b/model_lab.ipynb
new file mode 100644
index 0000000..29a0e41
--- /dev/null
+++ b/model_lab.ipynb
@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import keras\n",
+    "from keras import layers\n",
+    "from keras import models\n",
+    "from GenDs import GenDs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_step = 48\n",
+    "train_ds,std = GenDs.gen_train (time_step)\n",
+    "test_ds = GenDs.gen_test(time_step)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_shape = (time_step,2,16,8)\n",
+    "\n",
+    "inputs = layers.Input(shape = input_shape)\n",
+    "\n",
+    "\n",
+    "x = layers.Reshape((time_step,2*16*8))(inputs)\n",
+    "\n",
+    "x = layers.LSTM(512,dropout=0.2,recurrent_dropout=0.1, activation = \"relu\")(x)\n",
+    "\n",
+    "x = layers.Dense(2*16*8, activation = \"relu\")(x)\n",
+    "\n",
+    "output = layers.Reshape((2,16,8))(x)\n",
+    "\n",
+    "model = models.Model([inputs],outputs = output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model_1\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " input_2 (InputLayer)        [(None, 48, 2, 16, 8)]    0         \n",
+      "                                                                 \n",
+      " reshape_2 (Reshape)         (None, 48, 256)           0         \n",
+      "                                                                 \n",
+      " lstm_1 (LSTM)               (None, 512)               1574912   \n",
+      "                                                                 \n",
+      " dense_1 (Dense)             (None, 256)               131328    \n",
+      "                                                                 \n",
+      " reshape_3 (Reshape)         (None, 2, 16, 8)          0         \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 1706240 (6.51 MB)\n",
+      "Trainable params: 1706240 (6.51 MB)\n",
+      "Non-trainable params: 0 (0.00 Byte)\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38/38 [==============================] - 24s 592ms/step - loss: 0.5819 - val_loss: 0.4353\n",
+      "Epoch 2/50\n",
+      "38/38 [==============================] - 23s 608ms/step - loss: 0.4770 - val_loss: 0.4164\n",
+      "Epoch 3/50\n",
+      "38/38 [==============================] - 21s 565ms/step - loss: 0.4479 - val_loss: 0.4052\n",
+      "Epoch 4/50\n",
+      "38/38 [==============================] - 24s 628ms/step - loss: 0.4374 - val_loss: 0.3989\n",
+      "Epoch 5/50\n",
+      "38/38 [==============================] - 24s 636ms/step - loss: 0.4282 - val_loss: 0.3942\n",
+      "Epoch 6/50\n",
+      "23/38 [=================>............] - ETA: 9s - loss: 0.4247 "
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[12], line 5\u001b[0m\n\u001b[0;32m      1\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmse\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# model.fit([X_train,X_train],y_train,epochs=50, batch_size=10, validation_data=([X_test,X_test], y_test))\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# model.fit(ds,epochs=50, batch_size=10, validation_data=(test_ds),shuffle=False)\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_ds\u001b[49m\u001b[43m,\u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest_ds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      6\u001b[0m model\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbase.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\yuyao\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32mc:\\Users\\yuyao\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:1783\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;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[0;32m   1775\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[0;32m   1776\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1777\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1780\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m   1781\u001b[0m ):\n\u001b[0;32m   1782\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m-> 1783\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1784\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[0;32m   1785\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
+      "File \u001b[1;32mc:\\Users\\yuyao\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32mc:\\Users\\yuyao\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:831\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    828\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    830\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 831\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    833\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    834\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
+      "File \u001b[1;32mc:\\Users\\yuyao\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:867\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    864\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    865\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m    866\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[1;32m--> 867\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    868\u001b[0m \u001b[43m      \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_no_variable_creation_config\u001b[49m\n\u001b[0;32m    869\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    870\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    871\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[0;32m    872\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[0;32m    873\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
+      "File \u001b[1;32mc:\\Users\\yuyao\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[0;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[0;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\yuyao\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\concrete_function.py:1264\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[0;32m   1260\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1261\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1262\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1263\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1264\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1265\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1266\u001b[0m     args,\n\u001b[0;32m   1267\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1268\u001b[0m     executing_eagerly)\n\u001b[0;32m   1269\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
+      "File \u001b[1;32mc:\\Users\\yuyao\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:217\u001b[0m, in \u001b[0;36mAtomicFunction.flat_call\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mflat_call\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m    216\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 217\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    218\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
+      "File \u001b[1;32mc:\\Users\\yuyao\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:252\u001b[0m, in \u001b[0;36mAtomicFunction.__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    250\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m    251\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 252\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    257\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    258\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[0;32m    259\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    260\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[0;32m    261\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[0;32m    262\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\yuyao\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1479\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m   1477\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m   1478\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1479\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1480\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1481\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1482\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1483\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1484\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1485\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1486\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1487\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m   1488\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1489\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1493\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m   1494\u001b[0m   )\n",
+      "File \u001b[1;32mc:\\Users\\yuyao\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:60\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     53\u001b[0m   \u001b[38;5;66;03m# Convert any objects of type core_types.Tensor to Tensor.\u001b[39;00m\n\u001b[0;32m     54\u001b[0m   inputs \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m     55\u001b[0m       tensor_conversion_registry\u001b[38;5;241m.\u001b[39mconvert(t)\n\u001b[0;32m     56\u001b[0m       \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(t, core_types\u001b[38;5;241m.\u001b[39mTensor)\n\u001b[0;32m     57\u001b[0m       \u001b[38;5;28;01melse\u001b[39;00m t\n\u001b[0;32m     58\u001b[0m       \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m inputs\n\u001b[0;32m     59\u001b[0m   ]\n\u001b[1;32m---> 60\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     61\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     62\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     63\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "model.compile(optimizer='adam', loss='mse')\n",
+    "# model.fit([X_train,X_train],y_train,epochs=50, batch_size=10, validation_data=([X_test,X_test], y_test))\n",
+    "# model.fit(ds,epochs=50, batch_size=10, validation_data=(test_ds),shuffle=False)\n",
+    "\n",
+    "model.fit(train_ds,epochs=50,shuffle=False,validation_data=test_ds)\n",
+    "model.save(\"base.h5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/model_performance.ipynb b/model_performance.ipynb
index d63ce09..0c1846f 100644
--- a/model_performance.ipynb
+++ b/model_performance.ipynb
@@ -2,20 +2,32 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
     "from GenDs import GenDs\n",
-    "from ModelPerformance import *\n",
+    "from ModelPerformance import std_inverse,plot_result,compare_result\n",
     "import keras\n",
     "from keras import layers\n",
-    "from keras import models\n"
+    "from keras import models\n",
+    "import numpy as np\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,26 +38,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"model_29\"\n",
+      "Model: \"model\"\n",
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " input_40 (InputLayer)       [(None, 48, 2, 16, 8)]    0         \n",
+      " input_1 (InputLayer)        [(None, 48, 2, 16, 8)]    0         \n",
       "                                                                 \n",
-      " reshape_47 (Reshape)        (None, 48, 256)           0         \n",
+      " reshape (Reshape)           (None, 48, 256)           0         \n",
       "                                                                 \n",
-      " lstm_32 (LSTM)              (None, 100)               142800    \n",
+      " lstm (LSTM)                 (None, 100)               142800    \n",
       "                                                                 \n",
-      " dense_30 (Dense)            (None, 256)               25856     \n",
+      " dense (Dense)               (None, 256)               25856     \n",
       "                                                                 \n",
-      " reshape_48 (Reshape)        (None, 2, 16, 8)          0         \n",
+      " reshape_1 (Reshape)         (None, 2, 16, 8)          0         \n",
       "                                                                 \n",
       "=================================================================\n",
       "Total params: 168656 (658.81 KB)\n",
@@ -53,16 +65,16 @@
       "Non-trainable params: 0 (0.00 Byte)\n",
       "_________________________________________________________________\n",
       "None\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 0.3998\n"
+      "6/6 [==============================] - 0s 12ms/step - loss: 0.3826\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "0.3998368978500366"
+       "0.38258907198905945"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -75,9 +87,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 140ms/step\n",
+      "50.180029113849734\n",
+      "1/1 [==============================] - 0s 34ms/step\n",
+      "56.82033128532766\n",
+      "1/1 [==============================] - 0s 24ms/step\n",
+      "23.381881222715727\n",
+      "1/1 [==============================] - 0s 21ms/step\n",
+      "47.64655154910395\n",
+      "1/1 [==============================] - 0s 19ms/step\n",
+      "56.868088637474486\n",
+      "1/1 [==============================] - 0s 19ms/step\n",
+      "27.372569103597968\n"
+     ]
+    }
+   ],
    "source": [
     "# print original mse\n",
     "for X,y in test_ds:\n",
@@ -87,11 +118,127 @@
     "    \n",
     "    # print(inverse_result.shape,inverse_y.shape)\n",
     "    compare_result(inverse_result,inverse_y)\n",
-    "    for i in range(32):\n",
-    "        plot_result(inverse_y,inverse_result,i,0)\n",
+    "\n",
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 18ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 19ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 20ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 20ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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SlPLnz0Ke0ejUG9gkckxlt9snJ43MkaS99yYamRN/0GNmToWZ7Nb1TzcyR5JiV/855BmtyS5nPgAAgFWUDwAAYBXlAwAAWEX5AAAAVlE+AACAVcbLR2FhoYYMGaKEhAR1795do0eP1p49e0zfDWAc2YVbkV24jfHy8Yc//EH5+fnatGmTPvjgA9XX1+uWW25RXV2d6bsCjCK7cCuyC7cx/jkfK1eubPL1kiVL1L17d23btk033nij6bsDjCG7cCuyC7cJ+4eMVVVVSZK6du16xtv9fr/8fn/w6+rq6nCvBLQI2YVbkV20dWF9wWkgENDjjz+u66+/XldcccUZjyksLFRSUlLwkpmZGc6VgBYhu3Arsgs3CGv5yM/P16effqo333zzrMcUFBSoqqoqeCkrKwvnSkCLkF24FdmFG4TtaZcpU6bovffe07p163TRRRed9TifzyefzxeuNYBWI7twK7ILtzBePhzH0aOPPqp33nlHa9euVa9evUzfBRAWZBduRXbhNsbLR35+vt544w39z//8jxISElReXi5JSkpKUlxcnOm7A4whu3Arsgu3Mf6aj4ULF6qqqkrDhw9Xenp68PLWW2+ZvivAKLILtyK7cJuwPO0CuBHZhVuRXbgNP9sFAABYRfkAAABWhf0TTiPJ2/viSK/QTMzfyo3MOXHLYCNzYv53q5E5TnmlkTn4Svl18UbmNF5XZWSOJDXWmnnh4qIpi4zMeXrGZCNzUjd8aWSOJDV+7VNDz5fj1BvYJHJqe5jJSfkN5p5KcmIajMzpMtDM33Mnl6YZmZO4zcz/TySpwXJ2OfMBAACsonwAAACrKB8AAMAqygcAALCK8gEAAKyifAAAAKsoHwAAwCrKBwAAsIryAQAArKJ8AAAAqygfAADAKsoHAACwivIBAACsonwAAACrKB8AAMAqygcAALCK8gEAAKyKjvQC4eSpPR7pFZqpvzjNyBzfxj1G5kSlm9mn4e/lRubgK91uPWBkzoEtGUbmSNLgm3YbmfPYCw8bmdOpPmBkjudotZE5kuSJDv2vVI/jSA0GlomQI3fVGZkTvyXByBxJqu3TaGbO2+lG5nQ+6DcyR6fqzcyR/exy5gMAAFhF+QAAAFZRPgAAgFWUDwAAYBXlAwAAWBX28vHMM8/I4/Ho8ccfD/ddAUaRXbgV2UVbF9bysWXLFr300ksaMGBAOO8GMI7swq3ILtwgbOWjtrZW9913n15++WV16dIlXHcDGEd24VZkF24RtvKRn5+v22+/XTk5Od96nN/vV3V1dZMLEElkF25FduEWYfmE0zfffFNFRUXasmXLOY8tLCzU7Nmzw7EG0GpkF25FduEmxs98lJWV6bHHHtPrr7+u2NjYcx5fUFCgqqqq4KWsrMz0SkCLkF24FdmF2xg/87Ft2zYdOnRIV199dfC6xsZGrVu3Ti+++KL8fr+8Xm/wNp/PJ5/PZ3oNoNXILtyK7MJtjJePm2++WTt37mxy3YQJE9SnTx899dRTTf4AAG0J2YVbkV24jfHykZCQoCuuuKLJdfHx8erWrVuz64G2hOzCrcgu3IZPOAUAAFaF5d0u37R27VobdwMYR3bhVmQXbRlnPgAAgFVWznxEitOpo5k58ed+61pLRR8y9GE+aSlm5tQeNzMHRu37ItnInFfvfsnIHElaXH6TkTnVfRrMzBkQMDKn07JyI3MkyZsS+p/LqMAp6bCBZSLEKe5kZM53x+wwMkeSNh3saWTO8dTORuYcu6yDkTnZa9ybXc58AAAAqygfAADAKsoHAACwivIBAACsonwAAACrKB8AAMAqygcAALCK8gEAAKyifAAAAKsoHwAAwCrKBwAAsIryAQAArKJ8AAAAqygfAADAKsoHAACwivIBAACsonwAAACroiO9QDgdHdLdyJwuWw4ZmWOSU15pZI6nU7yROTDrquz9RuZMWDXRyBxJ+s+R/2Vkzvb3LzcyJ32j38gckxorQ/9z2ejUG9gkcqLqPUbmfPy7gUbmSNKou/5oZM7vP77OyJzeS44ZmRMwMuUrtrPLmQ8AAGAV5QMAAFhF+QAAAFZRPgAAgFWUDwAAYFVYyseBAwf0gx/8QN26dVNcXJz69++vrVu3huOuAKPILtyK7MJNjL/V9ujRo7r++us1YsQIvf/++0pJSVFxcbG6dOli+q4Ao8gu3Irswm2Ml49nn31WmZmZevXVV4PX9erVy/TdAMaRXbgV2YXbGH/a5d1339XgwYM1duxYde/eXVdddZVefvnlsx7v9/tVXV3d5AJEAtmFW5FduI3x8vG3v/1NCxcuVO/evbVq1Sr96Ec/0tSpU7V06dIzHl9YWKikpKTgJTMz0/RKQIuQXbgV2YXbGC8fgUBAV199tebOnaurrrpKkydP1qRJk7Ro0aIzHl9QUKCqqqrgpayszPRKQIuQXbgV2YXbGC8f6enp6tu3b5PrLr/8cu3ff+afVeHz+ZSYmNjkAkQC2YVbkV24jfHycf3112vPnj1Nrvvss8/Us2dP03cFGEV24VZkF25jvHw88cQT2rRpk+bOnau9e/fqjTfe0OLFi5Wfn2/6rgCjyC7ciuzCbYyXjyFDhuidd97Rb37zG11xxRX62c9+pnnz5um+++4zfVeAUWQXbkV24TbGP+dDku644w7dcccd4RgNhBXZhVuRXbgJP9sFAABYRfkAAABWheVpl7aiPs4T6RWaaehu5i1t3vJKI3Ma/l5uZA7MqjieYGTOf478LyNzJOnmuEYjc06mmZnToaLOyJyAkSlf8fa9NOQZTqNf2m1gmQiJPWRmzi2T/mhmkKTkmBojc052d4zMaYtsZ5czHwAAwCrKBwAAsIryAQAArKJ8AAAAqygfAADAKsoHAACwivIBAACsonwAAACrKB8AAMAqygcAALCK8gEAAKyifAAAAKsoHwAAwCrKBwAAsIryAQAArKJ8AAAAqygfAADAquhILxBOKcs+NTLHyc40MkeSvHWnzAwytdP2XWbmIMh7eW95vb6QZhz9uJuRXd7ofK2ROZKUkvahkTldeh41Mieq5oSROQEjU77iqa4LfUbAb2CT82Miuw3xZnZZ8dkAM4Mk3XGJmf8XNPocI3OiakLPieTu7HLmAwAAWEX5AAAAVlE+AACAVZQPAABgFeUDAABYZbx8NDY2avr06erVq5fi4uKUnZ2tn/3sZ3IcM68SBsKF7MKtyC7cxvhbbZ999lktXLhQS5cuVb9+/bR161ZNmDBBSUlJmjp1qum7A4whu3Arsgu3MV4+/vjHP+p73/uebr/9dklSVlaWfvOb32jz5s2m7wowiuzCrcgu3Mb40y7XXXedVq9erc8++0yStGPHDm3YsEG5ublnPN7v96u6urrJBYgEsgu3IrtwG+NnPp5++mlVV1erT58+8nq9amxs1Jw5c3Tfffed8fjCwkLNnj3b9BpAq5FduBXZhdsYP/OxbNkyvf7663rjjTdUVFSkpUuX6pe//KWWLl16xuMLCgpUVVUVvJSVlZleCWgRsgu3IrtwG+NnPp588kk9/fTTuueeeyRJ/fv3V2lpqQoLC5WXl9fseJ/PJ58vtJ8lAJhAduFWZBduY/zMx/HjxxUV1XSs1+tVIGDyR+AA5pFduBXZhdsYP/MxatQozZkzRz169FC/fv30ySef6LnnntMPf/hD03cFGEV24VZkF25jvHzMnz9f06dP1yOPPKJDhw4pIyND//zP/6wZM2aYvivAKLILtyK7cBvj5SMhIUHz5s3TvHnzTI8Gworswq3ILtyGn+0CAACsMn7moy0J1NQYmRP1ubm3oR0b1c/InM6/+4uROTDPc9IvT6i13tDrBDeUXmxmkKTiqvFG5pw8FWNkzqmseCNzokrN/fl2/KdCnxGoN7DJ+TGR3cYOZnapP2nuf0//+9Z3jcyJGnjCyJxTWclG5rg5u5z5AAAAVlE+AACAVZQPAABgFeUDAABYRfkAAABWUT4AAIBVlA8AAGAV5QMAAFhF+QAAAFZRPgAAgFWUDwAAYBXlAwAAWEX5AAAAVlE+AACAVZQPAABgFeUDAABYRfkAAABWRUd6gbOJSuikKE+H0GZ0ijeyS6C2zsgcSer8u78YmWPse6upMTLHpKiEhNBnOKekCH1rnz2cqqjY2JBmJBc5RnYJHDSTE0kqu85nZE6n4hgjc2I+3W1kTqORKV+pGXZxyDMa6k9KvzOwzHn4/MG0kLObsb7eyC6nPg/t7/+vK7/ezH/lrmtCe2xOi/l0j5E5JrN74uqeIc9oaDgpfdiyYznzAQAArKJ8AAAAqygfAADAKsoHAACwivIBAACsanX5WLdunUaNGqWMjAx5PB6tWLGiye2O42jGjBlKT09XXFyccnJyVFxcbGpf4LyRXbgV2UV70+ryUVdXp4EDB2rBggVnvP3f//3f9cILL2jRokX605/+pPj4eI0cOVInT54MeVkgFGQXbkV20d60+nM+cnNzlZube8bbHMfRvHnz9G//9m/63ve+J0n61a9+pdTUVK1YsUL33HNPaNsCISC7cCuyi/bG6Gs+SkpKVF5erpycnOB1SUlJGjp0qDZu3HjG3+P3+1VdXd3kAthGduFWZBduZLR8lJeXS5JSU1ObXJ+amhq87ZsKCwuVlJQUvGRmZppcCWgRsgu3Irtwo4i/26WgoEBVVVXBS1lZWaRXAlqE7MKtyC4izWj5SEtLkyRVVFQ0ub6ioiJ42zf5fD4lJiY2uQC2kV24FdmFGxktH7169VJaWppWr14dvK66ulp/+tOfdO2115q8K8Aosgu3Irtwo1a/26W2tlZ79+4Nfl1SUqLt27era9eu6tGjhx5//HH9/Oc/V+/evdWrVy9Nnz5dGRkZGj16tMm9gVYju3Arsov2ptXlY+vWrRoxYkTw62nTpkmS8vLytGTJEv3Lv/yL6urqNHnyZB07dkzDhg3TypUrFRvij2kGQkV24VZkF+1Nq8vH8OHD5TjOWW/3eDz66U9/qp/+9KchLQaYRnbhVmQX7U3E3+0CAAAuLJQPAABgVaufdnGTQG1dpFdoLtvMh/kEPud9+W1VoENA8gVCmtEY4zWyS6PPY2SOJKnBzKzMFRXnPqgFGr88YmSOc+1AI3MkKWF36Ds1NPoNbHJ+AjGOFHP2p3dawp9kJrsNBrMbX2rm39mp7+8zMqehDWa3464zfyBdazQEWp5dznwAAACrKB8AAMAqygcAALCK8gEAAKyifAAAAKsoHwAAwCrKBwAAsIryAQAArKJ8AAAAqygfAADAKsoHAACwivIBAACsonwAAACrKB8AAMAqygcAALCK8gEAAKyifAAAAKuiI73A2QRqahXwxIQ0IyohwdAuNUbmSJK27zI3q50y8XgHnHoDm5wn5/9fQhAILfpBaR8fNTNIUvrbh43M8XQw9M0ZElN+zNisxq6dQp4RaAwxPCFojA3IiQuENiPGa2SX5KJjRuZIkuegmeyqPWe3e1LoMxr90hctO5YzHwAAwCrKBwAAsIryAQAArKJ8AAAAqygfAADAqlaXj3Xr1mnUqFHKyMiQx+PRihUrgrfV19frqaeeUv/+/RUfH6+MjAw98MADOnjwoMmdgfNCduFWZBftTavLR11dnQYOHKgFCxY0u+348eMqKirS9OnTVVRUpN/+9rfas2eP7rzzTiPLAqEgu3Arsov2ptWf85Gbm6vc3Nwz3paUlKQPPvigyXUvvviirrnmGu3fv189evRo9nv8fr/8fn/w6+rq6tauBLQI2YVbkV20N2F/zUdVVZU8Ho86d+58xtsLCwuVlJQUvGRmZoZ7JaBFyC7ciuyirQtr+Th58qSeeuopjR8/XomJiWc8pqCgQFVVVcFLWVlZOFcCWoTswq3ILtwgbB+vXl9fr3HjxslxHC1cuPCsx/l8Pvl8vnCtAbQa2YVbkV24RVjKx+k/AKWlpfroo4/O2r6Btobswq3ILtzEePk4/QeguLhYa9asUbdu3UzfBRAWZBduRXbhNq0uH7W1tdq7d2/w65KSEm3fvl1du3ZVenq6xowZo6KiIr333ntqbGxUeXm5JKlr167q0KGDuc2BViK7cCuyi/am1eVj69atGjFiRPDradOmSZLy8vI0a9Ysvfvuu5KkK6+8ssnvW7NmjYYPH37+mwIhIrtwK7KL9qbV5WP48OFyHOest3/bbUAkkV24FdlFe8PPdgEAAFaF7a22bUGgpibSK+AC1GWnV94O3pBmnEr0GNklsOOvRua0Zw0lpcZmecoM/JXq1Ic+4zyZyO6JFLJri5uzy5kPAABgFeUDAABYRfkAAABWUT4AAIBVlA8AAGAV5QMAAFhF+QAAAFZRPgAAgFWUDwAAYBXlAwAAWEX5AAAAVlE+AACAVZQPAABgFeUDAABYRfkAAABWUT4AAIBVlA8AAGBVdKQXOJuohE6K8nQIaUagpsbQNm1PVEKCkTnt+TGKlM6f+xUd7QlphndNkZFdor+TYWSOJJ3sk25kjm9LsZE5gRMnjcxx6k8ZmSNJTkND6DOc0Gecr25/rlV0dIj3v3mnkV3aYnajV28zMscTE9r/205zc3Y58wEAAKyifAAAAKsoHwAAwCrKBwAAsIryAQAArGp1+Vi3bp1GjRqljIwMeTwerVix4qzHPvzww/J4PJo3b14IKwJmkF24FdlFe9Pq8lFXV6eBAwdqwYIF33rcO++8o02bNikjw9zbpYBQkF24FdlFe9Pqz/nIzc1Vbm7utx5z4MABPfroo1q1apVuv/32814OMInswq3ILtob4x8yFggEdP/99+vJJ59Uv379znm83++X3+8Pfl1dXW16JaBFyC7ciuzCbYy/4PTZZ59VdHS0pk6d2qLjCwsLlZSUFLxkZmaaXgloEbILtyK7cBuj5WPbtm16/vnntWTJEnk8Lft46YKCAlVVVQUvZWVlJlcCWoTswq3ILtzIaPlYv369Dh06pB49eig6OlrR0dEqLS3Vj3/8Y2VlZZ3x9/h8PiUmJja5ALaRXbgV2YUbGX3Nx/3336+cnJwm140cOVL333+/JkyYYPKuAKPILtyK7MKNWl0+amtrtXfv3uDXJSUl2r59u7p27aoePXqoW7duTY6PiYlRWlqaLrvsstC3BUJAduFWZBftTavLx9atWzVixIjg19OmTZMk5eXlacmSJcYWA0wju3Arsov2ptXlY/jw4XIcp8XH79u3r7V3AYQF2YVbkV20N/xsFwAAYBXlAwAAWGX8E05NCdTUKuCJifQabVagpibSK+AsvOt2yNtGsus0NBibFbv3kJE5gVY8ffBtvN26GJkTqDtuZI4kBWprDUzxSGYeotbbtksiu2cVSEgwMicqvqOROW7OLmc+AACAVZQPAABgFeUDAABYRfkAAABWUT4AAIBVlA8AAGAV5QMAAFhF+QAAAFZRPgAAgFWUDwAAYBXlAwAAWEX5AAAAVlE+AACAVZQPAABgFeUDAABYRfkAAABWRUd6gW9yHEeS1KB6yYnwMnCtBtVL+keebGiL2XUCp4zN8gTMzAk4ZnaKCpj568vUPl/Nqg95RoNDdiWy2xJuzm6bKx81NTWSpA36fYQ3QXtQU1OjpKQka/cltbHsHor0AmFUE+kFwovsRnqBMCK78jg263ULBAIBHTx4UAkJCfJ4PGc8prq6WpmZmSorK1NiYqLlDS88bny8HcdRTU2NMjIyFBVl59nFlmRXcufj6VZufKzJLiR3PtatyW6bO/MRFRWliy66qEXHJiYmuuY/Snvgtsfb1r8aT2tNdiX3PZ5u5rbHmuziNLc91i3NLi84BQAAVlE+AACAVa4sHz6fTzNnzpTP54v0KhcEHm+zeDzt4bE2i8fTnvb+WLe5F5wCAID2zZVnPgAAgHtRPgAAgFWUDwAAYBXlAwAAWEX5AAAAVrmyfCxYsEBZWVmKjY3V0KFDtXnz5kiv1O7MmjVLHo+nyaVPnz6RXsv1yG74kd3wILvhdyFl13Xl46233tK0adM0c+ZMFRUVaeDAgRo5cqQOHWrPP4UoMvr166e///3vwcuGDRsivZKrkV17yK5ZZNeeCyW7risfzz33nCZNmqQJEyaob9++WrRokTp27KhXXnkl0qu1O9HR0UpLSwtekpOTI72Sq5Fde8iuWWTXngslu64qH6dOndK2bduUk5MTvC4qKko5OTnauHFjBDdrn4qLi5WRkaGLL75Y9913n/bv3x/plVyL7NpFds0hu3ZdKNl1Vfk4fPiwGhsblZqa2uT61NRUlZeXR2ir9mno0KFasmSJVq5cqYULF6qkpEQ33HCDampqIr2aK5Fde8iuWWTXngspu9GRXgBtU25ubvDXAwYM0NChQ9WzZ08tW7ZMDz30UAQ3A74d2YVbXUjZddWZj+TkZHm9XlVUVDS5vqKiQmlpaRHa6sLQuXNnXXrppdq7d2+kV3Elshs5ZDc0ZDdy2nN2XVU+OnTooEGDBmn16tXB6wKBgFavXq1rr702gpu1f7W1tfr888+Vnp4e6VVciexGDtkNDdmNnHadXcdl3nzzTcfn8zlLlixxdu3a5UyePNnp3LmzU15eHunV2pUf//jHztq1a52SkhLn448/dnJycpzk5GTn0KFDkV7NtciuHWTXPLJrx4WUXde95uPuu+9WZWWlZsyYofLycl155ZVauXJlsxdDITRffPGFxo8fry+//FIpKSkaNmyYNm3apJSUlEiv5lpk1w6yax7ZteNCyq7HcRwn0ksAAIALh6te8wEAANyP8gEAAKyifAAAAKsoHwAAwCrKBwAAsIryAQAArKJ8AAAAqygfAADAKsoHAACwivIBAACsonwAAACr/h8GZG8zpQMv+AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 22ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 21ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for X,y in test_ds:\n",
+    "    result = model.predict (X)\n",
+    "    inverse_result = std_inverse(std,result)\n",
+    "    inverse_y = std_inverse(std,y)\n",
+    "    for i in range(1):\n",
+    "        plot_result(inverse_y,inverse_result,i,0)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,