Delete solutions directory

solutions/classifier_pretrained_model.py

@@ -1,21 +0,0 @@
-model_VGG_fcm = km.Sequential()
-model_VGG_fcm.add(kl.Flatten(input_shape=features_train.shape[1:]))
-model_VGG_fcm.add(kl.Dense(64, activation='relu'))
-model_VGG_fcm.add(kl.Dropout(0.5))
-model_VGG_fcm.add(kl.Dense(1, activation='sigmoid'))
-
-model_VGG_fcm.compile(optimizer='rmsprop',
-              loss='binary_crossentropy',
-              metrics=['accuracy'])
-
-model_VGG_fcm.summary()
-
-
-train_labels = np.array([0] * int((N_train/2)) + [1] * int((N_train/2)))
-validation_labels = np.array([0] * int((N_val/2)) + [1] * int((N_val/2)))
-
-model_VGG_fcm.fit(features_train, train_labels,
-          epochs=epochs,
-          batch_size=batch_size,
-          validation_data=(features_validation, validation_labels))
-t_learning_VGG_fcm = te-ts

solutions/max_pooling.py

@@ -1,13 +0,0 @@
-conv_mp = km.Sequential([ kl.MaxPool2D(pool_size=(2,2))])
-
-img_in = np.expand_dims(x, 0)
-img_out = conv_mp.predict(img_in)
-
-fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, 5))
-ax0.imshow(img_in[0,:,:,0].astype(np.uint8),
-           cmap="binary");
-ax0.grid(False)
-
-ax1.imshow(img_out[0,:,:,0].astype(np.uint8),
-           cmap="binary");
-ax1.grid(False)

solutions/mnist_conv_architecture.py

@@ -1,23 +0,0 @@
-model = km.Sequential()
-model.add(kl.Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28,28, 1), data_format="channels_last"))
-model.add(kl.Conv2D(64, (3, 3), activation='relu'))
-model.add(kl.MaxPooling2D(pool_size=(2, 2)))
-model.add(kl.Dropout(0.25))
-model.add(kl.Flatten())
-model.add(kl.Dense(128, activation='relu'))
-model.add(kl.Dropout(0.5))
-model.add(kl.Dense(N_classes, activation='softmax'))
-# Résumé
-model.summary()
-# Apprentissage
-model.compile(loss="sparse_categorical_crossentropy",
-              optimizer=ko.Adadelta(),
-              metrics=['accuracy'])
-ts=time.time()
-model.fit(X_train_conv, Y_train,
-          batch_size=batch_size,
-          epochs=epochs,
-          verbose=1,
-          validation_data=(X_test_conv, Y_test))
-te=time.time()
-t_train_conv = te-ts

solutions/test_kaggle.py

@@ -1,29 +0,0 @@
-data_dir_test = data_dir+'test/'
-N_test = len(os.listdir(data_dir_test+"/test"))
-
-test_datagen = kpi.ImageDataGenerator(rescale=1. / 255)
-
-test_generator = test_datagen.flow_from_directory(
-    data_dir_test,
-    #data_dir_sub+"/train/",
-    target_size=(img_height, img_width),
-    batch_size=batch_size,
-    class_mode=None,
-    shuffle=False)
-
-test_prediction = model_VGG_LastConv_fcm.predict_generator(test_generator, N_test // batch_size)
-
-images_test = [data_dir_test+"/test/"+k for k in os.listdir(data_dir_test+"/test")][:9]
-x_test  = [kpi.img_to_array(kpi.load_img(image_test))/255 for image_test in images_test]  # this is a PIL image
-
-fig = plt.figure(figsize=(10,10))
-for k in range(9):
-    ax = fig.add_subplot(3,3,k+1)
-    ax.imshow(x_test[k], interpolation='nearest')
-    pred = test_prediction[k]
-    if pred >0.5:
-        title = "Probabiliy for dog : %.1f" %(pred*100)
-    else:
-        title = "Probabiliy for cat : %.1f" %((1-pred)*100)
-    ax.set_title(title)
-plt.show()