plot donnees

code/resnet18/resnet18_snake.py

@@ -1,6 +1,7 @@
 # Réseau inspiré de http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
 
 
+from keras.callbacks import History
 from resnet18 import ResNet18
 import tensorflow as tf
 import numpy as np
@@ -12,7 +13,7 @@ import os
 
 def displayConvFilers(model, layer_name, num_filter=4, filter_size=(3,3), num_channel=0, fig_size=(2,2)):
     
-    layer_dict = dict([(layer.name, layer) for layer in mnist_model.layers])
+    layer_dict = dict([(layer.name, layer) for layer in model.layers])
     
     weight, biais = layer_dict[layer_name].get_weights()
     print(weight.shape) 
@@ -34,23 +35,48 @@ def snake(x):
 ## Chargement et normalisation des données
 resnet18 = tf.keras.datasets.cifar10
 (train_images, train_labels), (test_images, test_labels) = resnet18.load_data()
+
+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(50000,32,32,3)
+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)
 
 
 # One hot encoding
 train_labels = tf.keras.utils.to_categorical(train_labels)
+val_labels = tf.keras.utils.to_categorical(val_labels)
 test_labels = tf.keras.utils.to_categorical(test_labels)
 
 filter_size_conv1 = (3,3)
 
 model = ResNet18(10)
 model.build(input_shape = (None,32,32,3))
+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
+model.add(tf.keras.layers.Conv2D(filters=6,kernel_size=filter_size_conv1,padding="same", activation=snake, input_shape=(32, 32, 3)))
+model.add(tf.keras.layers.AveragePooling2D())
+model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=(5,5),padding="valid", activation=snake))
+model.add(tf.keras.layers.AveragePooling2D())
+model.add(tf.keras.layers.Flatten())
+model.add(tf.keras.layers.Dense(120 , activation=snake))
+#mnist_model.add(tf.keras.layers.Dropout(0.5))
+model.add(tf.keras.layers.Dense(84 , activation=snake))
+model.add(tf.keras.layers.Dense(10 , activation='softmax'))
+'''
 
 # expliquer le nombre de paramètre de ce réseau
 print(model.summary())
@@ -61,26 +87,33 @@ sgd = tf.keras.optimizers.Adam()
 model.compile(sgd, loss='categorical_crossentropy', metrics=['accuracy'])
 
 # On visualise avant l'entrainement
-'''
-displayConvFilers(mnist_model, 'conv2d', 
-                    num_filter=6, 
-                    filter_size=filter_size_conv1, 
-                    num_channel=0, 
-                    fig_size=(2,3)
-                )
-'''
 
 
-model.fit(train_images,
+
+history = model.fit(train_images,
          train_labels,
          batch_size=64,
-         epochs=4
+         epochs=4,
+         validation_data=(val_images, val_labels),
          )
 
 ## Evaluation du modèle 
 test_loss, test_acc = model.evaluate(test_images, test_labels)
 print('Test accuracy:', test_acc)
 
+fig, axs = plt.subplots(2, 1, figsize=(15,15))
+
+axs[0].plot(history.history['loss'])
+axs[0].plot(history.history['val_loss'])
+axs[0].title.set_text('Training Loss vs Validation Loss')
+axs[0].legend(['Train', 'Val'])
+
+axs[1].plot(history.history['accuracy'])
+axs[1].plot(history.history['val_accuracy'])
+axs[1].title.set_text('Training Accuracy vs Validation Accuracy')
+axs[1].legend(['Train', 'Val'])
+plt.savefig('./resnet18snake.png')
+
 
 '''
 displayConvFilers(mnist_model,
@@ -92,11 +125,3 @@ displayConvFilers(mnist_model,
                 )
 '''
 
-'''
-displayConvFilers(mnist_model, 'conv2d_1', 
-                    num_filter=16, 
-                    filter_size=(5,5), 
-                    num_channel=1, 
-                    fig_size=(4,4)
-                )
-'''