diff --git a/共享单车数据集分析/十三组平时作业（实战）/人脸识别.ipynb b/共享单车数据集分析/十三组平时作业（实战）/人脸识别.ipynb
new file mode 100644
index 0000000..8904504
--- /dev/null
+++ b/共享单车数据集分析/十三组平时作业（实战）/人脸识别.ipynb
@@ -0,0 +1,259 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\anaconda\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "keras = tf.keras\n",
+    "layers = keras.layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1611 files belonging to 6 classes.\n",
+      "Using 1289 files for training.\n",
+      "Found 1611 files belonging to 6 classes.\n",
+      "Using 322 files for validation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_train = keras.utils.image_dataset_from_directory(\n",
+    "    r\"D:\\data\\gray_face_images\",\n",
+    "    color_mode=\"grayscale\",\n",
+    "    validation_split=0.2,\n",
+    "    subset=\"training\",\n",
+    "    seed=42,\n",
+    ")\n",
+    "data_val = keras.utils.image_dataset_from_directory(\n",
+    "    r\"D:\\data\\gray_face_images\",\n",
+    "    color_mode=\"grayscale\",\n",
+    "    validation_split=0.2,\n",
+    "    subset=\"validation\",\n",
+    "    seed=42,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def data_convert(x, y):\n",
+    "    return x / 255., tf.one_hot(y, depth=6)\n",
+    "data_train = data_train.map(data_convert)\n",
+    "data_val = data_val.map(data_convert)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(32, 256, 256, 1) (32, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for x, y in data_train:\n",
+    "    print(x.shape, y.shape)\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs = layers.Input(shape=(256,256,1), name='Input')\n",
+    "x = layers.Conv2D(filters=32, kernel_size=3,  activation='relu', name='Cov1')(inputs)\n",
+    "x = layers.MaxPool2D(pool_size=2, name='Pool1')(x)\n",
+    "x = layers.Dropout(rate=0.5, name='Dropout1')(x)\n",
+    "\n",
+    "x = layers.Conv2D(filters=64, kernel_size=3, activation='relu', name='Cov2')(x)\n",
+    "x = layers.MaxPool2D(pool_size=2, name='Pool2')(x)\n",
+    "x = layers.Dropout(rate=0.5, name='Dropout2')(x)\n",
+    "\n",
+    "x = layers.Conv2D(filters=64, kernel_size=3, activation='relu', name='Cov3')(x)\n",
+    "x = layers.MaxPool2D(pool_size=2, name='Pool3')(x)\n",
+    "x = layers.Dropout(rate=0.5, name='Dropout3')(x)\n",
+    "\n",
+    "x = layers.Flatten(name=\"Flatten\")(x)\n",
+    "x = layers.Dropout(rate=0.75, name='Dropout4')(x)\n",
+    "x = layers.Dense(units=512, activation='relu', name=\"Dense1\")(x)\n",
+    "outputs = layers.Dense(units=6, activation='softmax', name=\"Output\")(x)\n",
+    "model = keras.Model(inputs, outputs)\n",
+    "keras.utils.plot_model(model, show_shapes=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " Input (InputLayer)          [(None, 256, 256, 1)]     0         \n",
+      "                                                                 \n",
+      " Cov1 (Conv2D)               (None, 254, 254, 32)      320       \n",
+      "                                                                 \n",
+      " Pool1 (MaxPooling2D)        (None, 127, 127, 32)      0         \n",
+      "                                                                 \n",
+      " Dropout1 (Dropout)          (None, 127, 127, 32)      0         \n",
+      "                                                                 \n",
+      " Cov2 (Conv2D)               (None, 125, 125, 64)      18496     \n",
+      "                                                                 \n",
+      " Pool2 (MaxPooling2D)        (None, 62, 62, 64)        0         \n",
+      "                                                                 \n",
+      " Dropout2 (Dropout)          (None, 62, 62, 64)        0         \n",
+      "                                                                 \n",
+      " Cov3 (Conv2D)               (None, 60, 60, 64)        36928     \n",
+      "                                                                 \n",
+      " Pool3 (MaxPooling2D)        (None, 30, 30, 64)        0         \n",
+      "                                                                 \n",
+      " Dropout3 (Dropout)          (None, 30, 30, 64)        0         \n",
+      "                                                                 \n",
+      " Flatten (Flatten)           (None, 57600)             0         \n",
+      "                                                                 \n",
+      " Dropout4 (Dropout)          (None, 57600)             0         \n",
+      "                                                                 \n",
+      " Dense1 (Dense)              (None, 512)               29491712  \n",
+      "                                                                 \n",
+      " Output (Dense)              (None, 6)                 3078      \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 29,550,534\n",
+      "Trainable params: 29,550,534\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "41/41 [==============================] - 8s 75ms/step - loss: 2.1733 - categorical_accuracy: 0.2234 - val_loss: 1.7901 - val_categorical_accuracy: 0.1460\n",
+      "Epoch 2/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 1.3984 - categorical_accuracy: 0.4259 - val_loss: 0.7413 - val_categorical_accuracy: 0.8851\n",
+      "Epoch 3/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.5248 - categorical_accuracy: 0.8348 - val_loss: 0.2736 - val_categorical_accuracy: 0.9658\n",
+      "Epoch 4/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.2337 - categorical_accuracy: 0.9348 - val_loss: 0.1263 - val_categorical_accuracy: 0.9814\n",
+      "Epoch 5/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.1423 - categorical_accuracy: 0.9558 - val_loss: 0.0671 - val_categorical_accuracy: 0.9907\n",
+      "Epoch 6/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.0916 - categorical_accuracy: 0.9744 - val_loss: 0.0493 - val_categorical_accuracy: 0.9907\n",
+      "Epoch 7/10\n",
+      "41/41 [==============================] - 3s 64ms/step - loss: 0.0835 - categorical_accuracy: 0.9744 - val_loss: 0.0723 - val_categorical_accuracy: 0.9752\n",
+      "Epoch 8/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.0830 - categorical_accuracy: 0.9752 - val_loss: 0.0447 - val_categorical_accuracy: 0.9938\n",
+      "Epoch 9/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.0490 - categorical_accuracy: 0.9829 - val_loss: 0.1132 - val_categorical_accuracy: 0.9627\n",
+      "Epoch 10/10\n",
+      "41/41 [==============================] - 3s 64ms/step - loss: 0.0558 - categorical_accuracy: 0.9829 - val_loss: 0.0259 - val_categorical_accuracy: 0.9907\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x17bc55b5430>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
+    "              loss=keras.losses.CategoricalCrossentropy(from_logits=False),\n",
+    "              metrics=[keras.metrics.CategoricalAccuracy()])\n",
+    "model.fit(data_train, validation_data=data_val, epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}