tweek some parameters

code/resnet18/resnet18.py

@@ -77,12 +77,12 @@ 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 = x + tf.sin(x)**2 #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)):
@@ -36,20 +34,28 @@ def snake(x):
 resnet18 = tf.keras.datasets.cifar10
 (train_images, train_labels), (test_images, test_labels) = resnet18.load_data()
 
+
+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