Keras acc и val_acc высоки во время тренировок, но прогноз очень плохой - PullRequest
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Keras acc и val_acc высоки во время тренировок, но прогноз очень плохой

0 голосов
/ 25 мая 2019

Я пытаюсь создать CNN, который классифицирует изображения на 3 класса.- Я использую следующие версии:

  • Керас 2.2.4
  • Питон 3.6.8
  • тензор потока 1.13.1

Во время тренировки мой val_acc выше 90%.Но когда я запускаю предикат_генератор на других изображениях (я даже использовал проверочные изображения -> результат все еще случайный), результат для них является случайным.

Я только что напечатал слои, входы, выходы, model.summary () после загрузки модели и все было в порядке.

Я ожидаю, что вывод моих прогнозов будет лучше, чем этот ...

Это мой код: 

import cv2
import pandas
from keras.models import Sequential
from keras.models import load_model
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Activation
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras import optimizers
from keras.preprocessing.image import ImageDataGenerator
from keras import callbacks
from sklearn.model_selection import train_test_split
from time import gmtime, strftime

CURRENT_TIME = strftime("%Y-%m-%d_%H-%M-%S", gmtime())
NAME = "CNN-" + CURRENT_TIME
SIZE = 280

NUM_CLASSES = 3
epochs = 25
batch_size = 64

df = pandas.read_csv("./csv/sample_tile.csv", ";")
df.columns = ['filenames', 'category']
df['category'] = df['category'].astype(str)
df = df.sample(frac=1).reset_index(drop=True)

train_df, validation_df = train_test_split(df, test_size=0.30, random_state=42)

train_df = train_df.reset_index(drop=True)
validation_df = validate_df.reset_index(drop=True)


total_train = train_df.shape[0]
total_validate = validate_df.shape[0]


train_datagen=ImageDataGenerator(rescale=1./255.)
train_generator = train_datagen.flow_from_dataframe(
    train_df, 
    "data/", 
    x_col='filenames',
    y_col='category',
    target_size=(SIZE, SIZE),
    class_mode='categorical',
    batch_size=batch_size,
)

# -> Found 6358 images belonging to 3 classes.

validation_datagen = ImageDataGenerator(rescale=1./255)
validation_generator = validation_datagen.flow_from_dataframe(
    validation_df, 
    "data/", 
    x_col='filenames',
    y_col='category',
    target_size=(SIZE, SIZE),
    class_mode='categorical',
    batch_size=batch_size//2,
)

# -> Found 2697 images belonging to 3 classes.

earlystop = callbacks.EarlyStopping(patience=3, monitor='val_acc')

learning_rate_reduction = callbacks.ReduceLROnPlateau(monitor='val_acc', 
                                            patience=2, 
                                            verbose=1, 
                                            factor=0.5, 
                                            min_lr=0.00001)

tensorboard = callbacks.TensorBoard(log_dir='logs/{}'.format(NAME))

model_checkpoint = callbacks.ModelCheckpoint(
    filepath='model/checkpoint/' + NAME +'.h5',
    monitor='val_acc',
    save_best_only=True
)

callbacks = [earlystop, learning_rate_reduction, model_checkpoint, tensorboard]

def mode_architecture():
    model = Sequential()

    input_shape = (SIZE, SIZE, 3)
    model.add(Conv2D(32, (3, 3), 
                     input_shape=input_shape, 
                     padding='same',
                     name='layer_conv1'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2),
                           name='maxPool1'))

    model.add(Conv2D(64, (3, 3), 
                     padding='same', 
                     name='layer_conv2'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2),
                           name='maxPool2'))

    model.add(Conv2D(128, (3, 3), 
                     padding='same', 
                     name='layer_conv3'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2),
                           name='maxPool3'))
    model.add(Flatten())
    model.add(Dense(128,
                    activation='relu',
                    name='fc0'))
    model.add(Dropout(0.2))
    model.add(Dense(64,
                    activation='relu',
                    name='fc1'))
    model.add(Dropout(0.2))
    model.add(Dense(32,
                    activation='relu',
                    name='fc2'))

    model.add(Dropout(0.5))
    model.add(Dense(NUM_CLASSES))
    model.add(Activation('softmax'))

    sgd = optimizers.SGD(lr=0.001, momentum=0.5, decay=0.0, nesterov=False)

    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
    return model

model = mode_architecture()
model.summary()

Layer (type)                 Output Shape              Param #   
_________________________________________________________________
layer_conv1 (Conv2D)         (None, 280, 280, 32)      896       
_________________________________________________________________
activation_1 (Activation)    (None, 280, 280, 32)      0         
_________________________________________________________________
maxPool1 (MaxPooling2D)      (None, 140, 140, 32)      0         
_________________________________________________________________
layer_conv2 (Conv2D)         (None, 140, 140, 64)      18496     
_________________________________________________________________
activation_2 (Activation)    (None, 140, 140, 64)      0         
_________________________________________________________________
maxPool2 (MaxPooling2D)      (None, 70, 70, 64)        0         
_________________________________________________________________
layer_conv3 (Conv2D)         (None, 70, 70, 128)       73856     
_________________________________________________________________
activation_3 (Activation)    (None, 70, 70, 128)       0         
_________________________________________________________________
maxPool3 (MaxPooling2D)      (None, 35, 35, 128)       0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 156800)            0         
_________________________________________________________________
fc0 (Dense)                  (None, 128)               20070528  
_________________________________________________________________
dropout_1 (Dropout)          (None, 128)               0         
_________________________________________________________________
fc1 (Dense)                  (None, 64)                8256      
_________________________________________________________________
dropout_2 (Dropout)          (None, 64)                0         
_________________________________________________________________
fc2 (Dense)                  (None, 32)                2080      
_________________________________________________________________
dropout_3 (Dropout)          (None, 32)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 3)                 99        
_________________________________________________________________
activation_4 (Activation)    (None, 3)                 0         
_________________________________________________________________
Total params: 20,174,211
Trainable params: 20,174,211
Non-trainable params: 0
_________________________________________________________________

history = model.fit_generator(
    train_generator, 
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=total_validate//batch_size,
    steps_per_epoch=total_train//batch_size,
    verbose=1,
    callbacks=callbacks
)

Эпоха 25/25 -> потеря: 0.1062 - согласно: 0.9610 - val_loss: 0.0721 - val_acc:0,9780 

model.save("model/" + str(batch_size) + "_" + str(epochs) + "_" +  CURRENT_TIME + ".h5")

del model

model = load_model("weights/" + str(batch_size) + "_" + str(epochs) + "_" +  CURRENT_TIME + ".h5")

files_df = pandas.DataFrame(os.listdir('path/to/images/'))
files_df.columns = ['filenames']

#shuffle
files_df = files_df.sample(frac=1).reset_index(drop=True)
files_df = files_df.reset_index(drop=True)

files_datagen=ImageDataGenerator(rescale=1./255.)
files_generator = files_datagen.flow_from_dataframe(
            files_df, 
            path_to_image_slices, 
            x_col='filenames',
            target_size=(SIZE, SIZE),
            class_mode=None,
            batch_size=64,
            )
predictions = model.predict_generator(files_generator,
                                      steps=value//batch_size,
                                      verbose=1)

РЕДАКТИРОВАТЬ:

train_df_plot:

img

train_df['category'].value_counts().plot.bar()

validation_df сюжет:

img

validation_df['category'].value_counts().plot.bar()

Dataframe 

df = pandas.read_csv("./csv/sample_tile.csv", ";")

Именно так я строю результат 

# select the index with the maximum probability
test_list_df = pandas.DataFrame(prediction_test)
test_list_df.columns = ['class1', 'class2', 'class3']
test_list = test_list_df.values.tolist()
max_value_idx = test_list_df.idxmax(axis=1)

test_filenames = test_generator.filenames
results=pandas.DataFrame({"Filenames":test_filenames,
                      "Predictions":max_value_idx})
length = len(test_list)/2
length = int(length)

# settings
nrows, ncols = 4, 2  # array of sub-plots
figsize = [25, 25]     # figure size, inches

# create figure (fig), and array of axes (ax)
fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize)

# plot simple raster image on each sub-plot
for i, axi in enumerate(ax.flat):
    # i runs from 0 to (nrows*ncols-1)
    # axi is equivalent with ax[rowid][colid]
    img = cv2.imread('data/' + results.iloc[i]['Filenames'])
    #img_s = cv2.resize(img, (SIZE,SIZE))
    axi.imshow(img)
    # get indices of row/column
    rowid = i // ncols
    colid = i % ncols
    # write row/col indices as axes' title for identification
    axi.set_title("Row:"+str(rowid)+", Col:"+str(colid) + results.iloc[i]['Predictions'], size=20)

plt.tight_layout(True)
plt.show()
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