seojisoosoo / Algorithm_04

Algorithm_04

MODEL1

1-1. MODEL1

  if model_number == 1:
    model = keras.models.Sequential([
                keras.layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (28, 28,1)),  # layer 1 
                keras.layers.MaxPool2D((2,2)),                                                  # layer 2 
                keras.layers.Flatten(),
                keras.layers.Dense(10, activation = 'softmax')])                                # layer 3

1-2. Training with training loss and test accuracy

1-3. Images and corresponding probability that predicted Right

1-4. Images and corresponding probability that predicted Wrong

---

MODEL2

2-1. MODEL2

if model_number == 2:
    model = keras.models.Sequential([
                keras.layers.Conv2D(32, (3,3), activation = 'relu', input_shape=(28,28,1)),     # layer 1 
                keras.layers.MaxPool2D((2,2)),                                                  # layer 2
                keras.layers.Conv2D(64, (3,3), activation = 'relu'),                            # layer 3 
                keras.layers.MaxPool2D((2,2)),                                                  # layer 4
                keras.layers.Flatten(),
                keras.layers.Dense(10, activation = 'softmax')])                                # layer 5

2-2. Training with training loss and test accuracy

2-3. Images and corresponding probability that predicted Right

2-4. Images and corresponding probability that predicted Wrong

---

MODEL3

3-1. MODEL3

if model_number == 3: 
    model = keras.models.Sequential([
                keras.layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (28, 28,1)),  # layer 1
                keras.layers.MaxPool2D((2,2)),                                                  # layer 2
                keras.layers.Conv2D(64, (3,3), activation = 'relu'),                            # layer 3
                keras.layers.Conv2D(64, (3,3), activation = 'relu'),                            # layer 4
                keras.layers.MaxPool2D((2,2)),                                                  # layer 5
                keras.layers.Conv2D(128, (3,3), activation = 'relu'),                           # layer 6
                keras.layers.Flatten(),
                keras.layers.Dense(10, activation = 'softmax')])                                # layer 7

3-2. Training with training loss and test accuracy

3-3. Images and corresponding probability that predicted Right

3-4. Images and corresponding probability that predicted Wrong