Merge remote-tracking branch 'origin/Emilien'

README.md

@@ -21,4 +21,20 @@
  	 └── rapport.tex               # Fichier source du rapport
 ```
 
-## To do list
+## Emilien
+
+Cette partie s'intéresse à l'efficacité de la fonction d'activation snake sur une tâche complexe de machine learning non-périodique. En effet pour que snake soit intéressant il faut qu'elle fasse aussi bien dans la plupart des tâches de ML comme la classification.
+C'est pourquoi conformément à l'article un réseau de CNN ResNet18 est appliqué sur la base de données ciphar-10.
+
+Pour utiliser :
+
+Aller dans code/resnet18/
+
+---> le fichier ResNet18.py est le script POO implémentant le réseau, les classes crées sont :
+
+     -- ResnetBlock une superclasse dont hérite la classe ResNet18, créant les resblock faisant apportant l'info résiduelle via la fonction call
+     -- ResNet18 qui implémente la structure de couches de convolution, elle prend comme argument le nombre de classe possible. Soit 10 dans le cas de CIPHAR-10
+     
+Pour faire varier la fonction d'activation --> juste décommenter #tf.nn.relu(x) lignes 44, 54, 84 et commenter x + tf.sin(x)**2
+
+--> le fichier resnet_snake.ipynb est le notebook main python a exectuer faisant l'appel de toutes les fonctions ainsi que de la classe Resnet18

code/resnet18/resnet18.py

@@ -20,7 +20,6 @@ class ResnetBlock(Model):
         self.__strides = [2, 1] if down_sample else [1, 1]
 
         KERNEL_SIZE = (3, 3)
-        # use He initialization, instead of Xavier (a.k.a 'glorot_uniform' in Keras), as suggested in [2]
         INIT_SCHEME = "he_normal"
 
         self.conv_1 = Conv2D(self.__channels, strides=self.__strides[0],
@@ -32,8 +31,8 @@ class ResnetBlock(Model):
         self.merge = Add()
 
         if self.__down_sample:
-            # perform down sampling using stride of 2, according to [1].
-            self.res_conv = Conv2D(
+
+          self.res_conv = Conv2D(
                 self.__channels, strides=2, kernel_size=(1, 1), kernel_initializer=INIT_SCHEME, padding="same")
             self.res_bn = BatchNormalization()
 
@@ -42,7 +41,7 @@ class ResnetBlock(Model):
 
         x = self.conv_1(inputs)
         x = self.bn_1(x)
-        x = tf.nn.relu(x)
+        x = x + tf.sin(x)**2 #tf.nn.relu(x)
         x = self.conv_2(x)
         x = self.bn_2(x)
 
@@ -50,9 +49,9 @@ class ResnetBlock(Model):
             res = self.res_conv(res)
             res = self.res_bn(res)
 
-        # if not perform down sample, then add a shortcut directly
+        
         x = self.merge([x, res])
-        out = tf.nn.relu(x)
+        out = x + tf.sin(x)**2 #tf.nn.relu(x)
         return out
 
 
@@ -77,12 +76,12 @@ class ResNet18(Model):
         self.res_4_2 = ResnetBlock(512)
         self.avg_pool = GlobalAveragePooling2D()
         self.flat = Flatten()
-        self.fc = Dense(num_classes, activation="softmax")
+        self.fc = Dense(num_classes, activation="sigmoid")
 
     def call(self, inputs):
         out = self.conv_1(inputs)
         out = self.init_bn(out)
-        out = tf.nn.relu(out)
+        out += tf.sin(out)**2 #tf.nn.relu(out)
         out = self.pool_2(out)
         for res_block in [self.res_1_1, self.res_1_2, self.res_2_1, self.res_2_2, self.res_3_1, self.res_3_2, self.res_4_1, self.res_4_2]:
             out = res_block(out)

code/resnet18/resnet18_snake.py

@@ -1,14 +1,12 @@
 # Réseau inspiré de http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
 
 
-from keras.callbacks import History
+
+from tensorflow.python.ops.gen_array_ops import tensor_scatter_min_eager_fallback
 from resnet18 import ResNet18
 import tensorflow as tf
 import numpy as np
 import matplotlib.pyplot as plt
-# Pour les utilisateurs de MacOS  (pour utiliser plt & keras en même temps)
-import os
-#os.environ['KMP_DUPLICATE_LIB_OK']='True'
 
 
 def displayConvFilers(model, layer_name, num_filter=4, filter_size=(3,3), num_channel=0, fig_size=(2,2)):
@@ -35,21 +33,30 @@ def snake(x):
 ## Chargement et normalisation des données
 resnet18 = tf.keras.datasets.cifar10
 (train_images, train_labels), (test_images, test_labels) = resnet18.load_data()
+train_images = train_images.astype('float32')
+test_images = test_images.astype('float32')
+
+from sklearn.model_selection import train_test_split
+train_images, val_images, train_labels, val_labels = train_test_split(train_images,train_labels, test_size = 0.2,shuffle = True)
+
+
 
+'''
 val_images = train_images[40000:]
 val_labels = train_labels[40000:]
 
 train_images = train_images[:40000]
 train_labels = train_labels[:40000]
+'''
 
 train_images = train_images / 255.0
 val_images = val_images /255.0
 test_images = test_images / 255.0
 
 # POUR LES CNN : On rajoute une dimension pour spécifier qu'il s'agit d'imgages en NdG
-train_images = train_images.reshape(40000,32,32,3)
-val_images = val_images.reshape(10000,32,32,3)
-test_images = test_images.reshape(10000,32,32,3)
+train_images = train_images.reshape(max(np.shape(train_images)),32,32,3)
+val_images = val_images.reshape(max(np.shape(val_images)),32,32,3)
+test_images = test_images.reshape(max(np.shape(test_images)),32,32,3)
 
 
 # One hot encoding
@@ -61,9 +68,9 @@ filter_size_conv1 = (3,3)
 
 model = ResNet18(10)
 model.build(input_shape = (None,32,32,3))
-filter_size_conv1 = (5,5)
 
 '''
+filter_size_conv1 = (5,5)
 ## Définition de l'architecture du modèle
 model = tf.keras.models.Sequential()
 # Expliquez à quoi correspondent les valeurs numériques qui définissent les couches du réseau
@@ -92,8 +99,8 @@ model.compile(sgd, loss='categorical_crossentropy', metrics=['accuracy'])
 
 history = model.fit(train_images,
          train_labels,
-         batch_size=64,
-         epochs=4,
+         batch_size=256,
+         epochs=50,
          validation_data=(val_images, val_labels),
          )
 

code/resnet18/resnet_snake.ipynb

@@ -0,0 +1,151 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Untitled1.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hUYO09lse0Ew"
+      },
+      "outputs": [],
+      "source": [
+        "from tensorflow.python.ops.gen_array_ops import tensor_scatter_min_eager_fallback\n",
+        "from resnet18 import ResNet18\n",
+        "import tensorflow as tf\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def snake(x):\n",
+        "    return x + tf.sin(x)**2\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "## Chargement et normalisation des données\n",
+        "resnet18 = tf.keras.datasets.cifar10\n",
+        "(train_images, train_labels), (test_images, test_labels) = resnet18.load_data()\n",
+        "train_images = train_images.astype('float32')\n",
+        "test_images = test_images.astype('float32')\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "train_images, val_images, train_labels, val_labels = train_test_split(train_images,train_labels, test_size = 0.2,shuffle = True)\n",
+        "\n",
+        "train_images = train_images / 255.0\n",
+        "val_images = val_images /255.0\n",
+        "test_images = test_images / 255.0\n",
+        "\n",
+        "# POUR LES CNN : un tenseur d'ordre 3 pour les images en couleurs\n",
+        "train_images = train_images.reshape(max(np.shape(train_images)),32,32,3)\n",
+        "val_images = val_images.reshape(max(np.shape(val_images)),32,32,3)\n",
+        "test_images = test_images.reshape(max(np.shape(test_images)),32,32,3)\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "-iczeI91fEND"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# One hot encoding\n",
+        "train_labels = tf.keras.utils.to_categorical(train_labels)\n",
+        "val_labels = tf.keras.utils.to_categorical(val_labels)\n",
+        "test_labels = tf.keras.utils.to_categorical(test_labels)\n",
+        "\n",
+        "filter_size_conv1 = (3,3)\n",
+        "\n",
+        "\n",
+        "#création du réseau ResNet18\n",
+        "\n",
+        "model = ResNet18(10)\n",
+        "model.build(input_shape = (None,32,32,3))\n",
+        "print(model.summary())\n"
+      ],
+      "metadata": {
+        "id": "9UGNJFoRfGTz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Adam comme optimizer et categorical-crossentropy comme norme\n",
+        "sgd = tf.keras.optimizers.Adam()\n",
+        "model.compile(sgd, loss='categorical_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "\n",
+        "\n",
+        "history = model.fit(train_images,\n",
+        "         train_labels,\n",
+        "         batch_size=64,\n",
+        "         epochs=100,\n",
+        "         validation_data=(val_images, val_labels),\n",
+        "         )\n"
+      ],
+      "metadata": {
+        "id": "PtY0q1p5fM-p"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "## Evaluation du modèle \n",
+        "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
+        "print('Test accuracy:', test_acc)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "FcehZmotfPCx"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "## on affiche et on sauvegarde les images\n",
+        "\n",
+        "fig, axs = plt.subplots(2, 1, figsize=(15,15))\n",
+        "\n",
+        "axs[0].plot(history.history['loss'])\n",
+        "axs[0].plot(history.history['val_loss'])\n",
+        "axs[0].title.set_text('Training Loss vs Validation Loss')\n",
+        "axs[0].legend(['Train', 'Val'])\n",
+        "\n",
+        "axs[1].plot(history.history['accuracy'])\n",
+        "axs[1].plot(history.history['val_accuracy'])\n",
+        "axs[1].title.set_text('Training Accuracy vs Validation Accuracy')\n",
+        "axs[1].legend(['Train', 'Val'])\n",
+        "plt.savefig('./resnet18snake.png')"
+      ],
+      "metadata": {
+        "id": "8USwL2YTfRGN"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}