Update Cap12

Author: DanielDrabeski

Cap12/DSA-Python-Cap12-01-Deep-Learning-Treinamento.ipynb

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+model_checkpoint_path: "model.ckpt-900"
+all_model_checkpoint_paths: "model.ckpt-500"
+all_model_checkpoint_paths: "model.ckpt-600"
+all_model_checkpoint_paths: "model.ckpt-700"
+all_model_checkpoint_paths: "model.ckpt-800"
+all_model_checkpoint_paths: "model.ckpt-900"

Cap12/DSA-Python-Cap12-02-Deep-Learning-Teste.ipynb

@@ -0,0 +1,135 @@
+# Imports
+import pandas as pd
+import numpy as np
+import os, sys, inspect
+from six.moves import cPickle as pickle
+import scipy.misc as misc
+
+# Parâmetros
+IMAGE_SIZE = 48
+NUM_LABELS = 7
+
+# Usando 10% dos dados para validação
+VALIDATION_PERCENT = 0.1  
+
+# Normalização
+IMAGE_LOCATION_NORM = IMAGE_SIZE // 2
+
+# Seed
+np.random.seed(0)
+
+# For training 
+train_error_list = []
+train_step_list = []
+
+# For validation 
+valid_error_list = []
+valid_step_list = []
+
+# Emoções
+emotion = {0:'anger', 
+           1:'disgust',
+           2:'fear',
+           3:'happy',
+           4:'sad',
+           5:'surprise',
+           6:'neutral'}
+
+# Classe para o resultado em teste
+class testResult:
+
+    def __init__(self):
+        self.anger = 0
+        self.disgust = 0
+        self.fear = 0
+        self.happy = 0
+        self.sad = 0
+        self.surprise = 0
+        self.neutral = 0
+        
+    def evaluate(self,label):        
+        if (0 == label):
+            self.anger = self.anger+1
+        if (1 == label):
+            self.disgust = self.disgust+1
+        if (2 == label):
+            self.fear = self.fear+1
+        if (3 == label):
+            self.happy = self.happy+1
+        if (4 == label):
+            self.sad = self.sad+1
+        if (5 == label):
+            self.surprise = self.surprise+1
+        if (6 == label):
+            self.neutral = self.neutral+1
+
+    def display_result(self,evaluations):
+        print("anger = "    + str((self.anger/float(evaluations))*100)    + "%")
+        print("disgust = "  + str((self.disgust/float(evaluations))*100)  + "%")
+        print("fear = "     + str((self.fear/float(evaluations))*100)     + "%")
+        print("happy = "    + str((self.happy/float(evaluations))*100)    + "%")
+        print("sad = "      + str((self.sad/float(evaluations))*100)      + "%")
+        print("surprise = " + str((self.surprise/float(evaluations))*100) + "%")
+        print("neutral = "  + str((self.neutral/float(evaluations))*100)  + "%")      
+
+
+# Função para leitura dos dados
+def read_data(data_dir, force=False):
+    def create_onehot_label(x):
+        label = np.zeros((1, NUM_LABELS), dtype=np.float32)
+        label[:, int(x)] = 1
+        return label
+
+    pickle_file = os.path.join(data_dir, "EmotionDetectorData.pickle")
+    if force or not os.path.exists(pickle_file):
+        train_filename = os.path.join(data_dir, "train.csv")
+        data_frame = pd.read_csv(train_filename)
+        data_frame['Pixels'] = data_frame['Pixels'].apply(lambda x: np.fromstring(x, sep=" ") / 255.0)
+        data_frame = data_frame.dropna()
+        print("Lendo train.csv ...")
+
+        train_images = np.vstack(data_frame['Pixels']).reshape(-1, IMAGE_SIZE, IMAGE_SIZE, 1)
+        print(train_images.shape)
+        train_labels = np.array(list(map(create_onehot_label, data_frame['Emotion'].values))).reshape(-1, NUM_LABELS)
+        print(train_labels.shape)
+
+        permutations = np.random.permutation(train_images.shape[0])
+        train_images = train_images[permutations]
+        train_labels = train_labels[permutations]
+        validation_percent = int(train_images.shape[0] * VALIDATION_PERCENT)
+        validation_images = train_images[:validation_percent]
+        validation_labels = train_labels[:validation_percent]
+        train_images = train_images[validation_percent:]
+        train_labels = train_labels[validation_percent:]
+
+        print("Lendo test.csv ...")
+        test_filename = os.path.join(data_dir, "test.csv")
+        data_frame = pd.read_csv(test_filename)
+        data_frame['Pixels'] = data_frame['Pixels'].apply(lambda x: np.fromstring(x, sep=" ") / 255.0)
+        data_frame = data_frame.dropna()
+        test_images = np.vstack(data_frame['Pixels']).reshape(-1, IMAGE_SIZE, IMAGE_SIZE, 1)
+
+        with open(pickle_file, "wb") as file:
+            try:
+                print('\nSalvando ...')
+                save = {
+                    "train_images": train_images,
+                    "train_labels": train_labels,
+                    "validation_images": validation_images,
+                    "validation_labels": validation_labels,
+                    "test_images": test_images,
+                }
+                pickle.dump(save, file, pickle.HIGHEST_PROTOCOL)
+
+            except:
+                print("Não foi possível salvar :/")
+
+    with open(pickle_file, "rb") as file:
+        save = pickle.load(file)
+        train_images = save["train_images"]
+        train_labels = save["train_labels"]
+        validation_images = save["validation_images"]
+        validation_labels = save["validation_labels"]
+        test_images = save["test_images"]
+
+    return train_images, train_labels, validation_images, validation_labels, test_images