I've spend the last 2 weeks struggling with my NN. The aim is to predict trip durations of taxi courses based on several
numerical variables (latitudes and longitudes)
categorical variables (numerically encoded) (hour of the day, day of the week, etc)
Here is the simplest version
X_train = trainData.as_matrix(columns=["fareDistance","hour","day","pickup_longitude","pickup_latitude","dropoff_longitude","dropoff_latitude"])
Y_train = np.array(trainData["trip_duration"])
model = Sequential()
model.add(Dense(32, input_dim=7, activation='linear'))
model.add(Dense(12, activation='linear'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mean_absolute_percentage_error', optimizer='adagrad', metrics=['accuracy'])
model.summary()
model.fit(X_train, Y_train, epochs=10, validation_split=0.2)
I also tried to merge two different models for numerical variables on one hand and categorical on the other but it didn't change a thing. Depending on the combinations of Loss and optimization function either the loss and accuracy remain quite the same (acc. 0.0016) or I don't even have non null acc.
A friend of mine replicated the NN in pure TensorFlow and got the same kind of results
Train on 233383 samples, validate on 58346 samples
Epoch 1/20 233383/233383 [==============================] - 15s - loss: 45.9550 - acc: 0.0016 - val_loss: 46.2514 - val_acc: 0.0014
Epoch 2/20 233383/233383 [==============================] - 15s - loss: 45.8675 - acc: 0.0014 - val_loss: 46.2675 - val_acc: 0.0015
Epoch 3/20 233383/233383 [==============================] - 15s - loss: 45.8465 - acc: 0.0015 - val_loss: 46.2131 - val_acc: 0.0013
Epoch 4/20 233383/233383 [==============================] - 15s - loss: 45.8283 - acc: 0.0014 - val_loss: 46.2478 - val_acc: 0.0016
Epoch 5/20 233383/233383 [==============================] - 15s - loss: 45.8214 - acc: 0.0015 - val_loss: 46.2043 - val_acc: 0.0013
Epoch 6/20 233383/233383 [==============================] - 14s - loss: 45.8122 - acc: 0.0014 - val_loss: 46.2526 - val_acc: 0.0014
Epoch 7/20 233383/233383 [==============================] - 12s - loss: 45.7990 - acc: 0.0015 - val_loss: 46.1821 - val_acc: 0.0014
Epoch 8/20 233383/233383 [==============================] - 12s - loss: 45.7964 - acc: 0.0016 - val_loss: 46.1761 - val_acc: 0.0013
Epoch 9/20 233383/233383 [==============================] - 11s - loss: 45.7898 - acc: 0.0015 - val_loss: 46.1804 - val_acc: 0.0016
Am I missing something -- like something big, obvious -- which would explain why any attempt to change activation, loss or optimization function ends up doing the same?
Thanks in advance
D.
try this:
X_train = trainData.as_matrix(columns=["fareDistance","hour","day","pickup_longitude","pickup_latitude","dropoff_longitude","dropoff_latitude"])
Y_train = np.array(trainData["trip_duration"])
model = Sequential()
model.add(Dense(32, input_dim=7, activation='elu'))
model.add(Dense(12, activation='elu'))
model.add(Dense(1, kernel_initializer='normal'))
model.compile(loss='mean_absolute_percentage_error', optimizer='rmsprop')
model.summary()
model.fit(X_train, Y_train, epochs=10, validation_split=0.2)
you can also try the adam optimizer.
model.compile(loss='mean_absolute_percentage_error', optimizer='adam')
Update:
If the code above didn't help you it means your input data either not normalized or very dirty.
Related
First of all, I know that there is a similar thread here:
https://stats.stackexchange.com/questions/352036/what-should-i-do-when-my-neural-network-doesnt-learn
But unfortunately, it does not help. I probably have a bug inside my code which I cannot find. What I am trying to do is to classify some WAV files. But the model does not learn.
At first, I am collecting the files and saving them in an array.
Second, create new directories, one for train data and one for val data.
Next, I am reading the WAV files, creating spectrograms, and saving them all to the train directory.
Afterward, I am moving 20% of the data from the train directory to the val directory.
Note: While creating the spectrograms I am checking the length of the WAV. If it is too short (less than 2 sec), I am doubling it. Out of this spectrogram, I am cutting a random chunk and saving only this. As a result, all images do have the same height and width.
Then as the next step, I am loading the train and val images. And here I am also doing the normalization.
IMG_WIDTH=300
IMG_HEIGHT=300
IMG_DIM = (IMG_WIDTH, IMG_HEIGHT, 3)
train_files = glob.glob(DBMEL_PATH + "*",recursive=True)
train_imgs = [img_to_array(load_img(img, target_size=IMG_DIM)) for img in train_files]
train_imgs = np.array(train_imgs) / 255 # normalizing Data
train_labels = [fn.split('\\')[-1].split('.')[1].strip() for fn in train_files]
validation_files = glob.glob(DBMEL_VAL_PATH + "*",recursive=True)
validation_imgs = [img_to_array(load_img(img, target_size=IMG_DIM)) for img in validation_files]
validation_imgs = np.array(validation_imgs) / 255 # normalizing Data
validation_labels = [fn.split('\\')[-1].split('.')[1].strip() for fn in validation_files]
I have checked the variables and printing them. I guess this is working quite well. The arrays contain 80% and respectively 20% of the total data.
#Train dataset shape: (3756, 300, 300, 3)
#Validation dataset shape: (939, 300, 300, 3)
Next, I have also implemented a One-Hot-Encoder.
So far so good. In the next step I create empty DataGenerators, so without any data augmentation. When calling the DataGenerators, one time for train-data and one time for val-data, I'll pass the arrays for images (train_imgs, validation_imgs) and the one-hot-encoded-labels (train_labels_enc, validation_labels_enc).
Okay. Here now comes the tricky part.
First, create/load a pre-trained network
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.models import Model
import tensorflow.keras
input_shape=(IMG_HEIGHT,IMG_WIDTH,3)
restnet = ResNet50(include_top=False, weights='imagenet', input_shape=(IMG_HEIGHT,IMG_WIDTH,3))
output = restnet.layers[-1].output
output = tensorflow.keras.layers.Flatten()(output)
restnet = Model(restnet.input, output)
for layer in restnet.layers:
layer.trainable = False
And now finally creating the model itself. While creating the model I am using the pre-trained network for transfer learning. I guess somewhere there must be a problem.
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, InputLayer
from tensorflow.keras.models import Sequential
from tensorflow.keras import optimizers
model = Sequential()
model.add(restnet) # <-- transfer learning
model.add(Dense(512, activation='relu', input_dim=input_shape))# 512 (num_classes)
model.add(Dropout(0.3))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(7, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
And the models run with this
history = model.fit_generator(train_generator,
steps_per_epoch=100,
epochs=100,
validation_data=val_generator,
validation_steps=10,
verbose=1
)
But even after 50 epochs the accuracy stalls at around 0.15
Epoch 1/100
100/100 [==============================] - 711s 7s/step - loss: 10.6419 - accuracy: 0.1530 - val_loss: 1.9416 - val_accuracy: 0.1467
Epoch 2/100
100/100 [==============================] - 733s 7s/step - loss: 1.9595 - accuracy: 0.1550 - val_loss: 1.9372 - val_accuracy: 0.1267
Epoch 3/100
100/100 [==============================] - 731s 7s/step - loss: 1.9940 - accuracy: 0.1444 - val_loss: 1.9388 - val_accuracy: 0.1400
Epoch 4/100
100/100 [==============================] - 735s 7s/step - loss: 1.9416 - accuracy: 0.1535 - val_loss: 1.9380 - val_accuracy: 0.1733
Epoch 5/100
100/100 [==============================] - 737s 7s/step - loss: 1.9394 - accuracy: 0.1656 - val_loss: 1.9345 - val_accuracy: 0.1533
Epoch 6/100
100/100 [==============================] - 741s 7s/step - loss: 1.9364 - accuracy: 0.1667 - val_loss: 1.9286 - val_accuracy: 0.1767
Epoch 7/100
100/100 [==============================] - 740s 7s/step - loss: 1.9389 - accuracy: 0.1523 - val_loss: 1.9305 - val_accuracy: 0.1400
Epoch 8/100
100/100 [==============================] - 737s 7s/step - loss: 1.9394 - accuracy: 0.1623 - val_loss: 1.9441 - val_accuracy: 0.1667
Epoch 9/100
100/100 [==============================] - 735s 7s/step - loss: 1.9391 - accuracy: 0.1582 - val_loss: 1.9458 - val_accuracy: 0.1333
Epoch 10/100
100/100 [==============================] - 734s 7s/step - loss: 1.9381 - accuracy: 0.1602 - val_loss: 1.9372 - val_accuracy: 0.1700
Epoch 11/100
100/100 [==============================] - 739s 7s/step - loss: 1.9392 - accuracy: 0.1623 - val_loss: 1.9302 - val_accuracy: 0.2167
Epoch 12/100
100/100 [==============================] - 741s 7s/step - loss: 1.9368 - accuracy: 0.1627 - val_loss: 1.9326 - val_accuracy: 0.1467
Epoch 13/100
100/100 [==============================] - 740s 7s/step - loss: 1.9381 - accuracy: 0.1513 - val_loss: 1.9312 - val_accuracy: 0.1733
Epoch 14/100
100/100 [==============================] - 736s 7s/step - loss: 1.9396 - accuracy: 0.1542 - val_loss: 1.9407 - val_accuracy: 0.1367
Epoch 15/100
100/100 [==============================] - 741s 7s/step - loss: 1.9393 - accuracy: 0.1597 - val_loss: 1.9336 - val_accuracy: 0.1333
Epoch 16/100
100/100 [==============================] - 737s 7s/step - loss: 1.9375 - accuracy: 0.1659 - val_loss: 1.9354 - val_accuracy: 0.1267
Epoch 17/100
100/100 [==============================] - 741s 7s/step - loss: 1.9422 - accuracy: 0.1487 - val_loss: 1.9307 - val_accuracy: 0.1567
Epoch 18/100
100/100 [==============================] - 738s 7s/step - loss: 1.9399 - accuracy: 0.1680 - val_loss: 1.9408 - val_accuracy: 0.1567
Epoch 19/100
100/100 [==============================] - 743s 7s/step - loss: 1.9405 - accuracy: 0.1610 - val_loss: 1.9335 - val_accuracy: 0.1533
Epoch 20/100
100/100 [==============================] - 738s 7s/step - loss: 1.9410 - accuracy: 0.1575 - val_loss: 1.9331 - val_accuracy: 0.1533
Epoch 21/100
100/100 [==============================] - 746s 7s/step - loss: 1.9395 - accuracy: 0.1639 - val_loss: 1.9344 - val_accuracy: 0.1733
Epoch 22/100
100/100 [==============================] - 746s 7s/step - loss: 1.9393 - accuracy: 0.1585 - val_loss: 1.9354 - val_accuracy: 0.1667
Epoch 23/100
100/100 [==============================] - 746s 7s/step - loss: 1.9398 - accuracy: 0.1599 - val_loss: 1.9352 - val_accuracy: 0.1500
Epoch 24/100
100/100 [==============================] - 746s 7s/step - loss: 1.9392 - accuracy: 0.1585 - val_loss: 1.9449 - val_accuracy: 0.1667
Epoch 25/100
100/100 [==============================] - 746s 7s/step - loss: 1.9399 - accuracy: 0.1495 - val_loss: 1.9352 - val_accuracy: 0.1600
Can anyone please help to find the problem?
I solved the problem on my own.
I exchanged this
model = Sequential()
model.add(restnet) # <-- transfer learning
model.add(Dense(512, activation='relu', input_dim=input_shape))# 512 (num_classes)
model.add(Dropout(0.3))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(7, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
with this:
base_model = tf.keras.applications.MobileNetV2(input_shape = (224, 224, 3), include_top = False, weights = "imagenet")
model = Sequential()
model.add(base_model)
model.add(tf.keras.layers.GlobalAveragePooling2D())
model.add(Dropout(0.2))
model.add(Dense(number_classes, activation="softmax"))
model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.00001),
loss="categorical_crossentropy",
metrics=['accuracy'])
model.summary()
And I found out one more thing. In contrary to some tutorials, using data augmentation is not useful when working with spectrograms.
Without data augmentation I got 0.99 on train-accuracy and 0.72 on val-accuracy. But with data augmentation I got only 0.75 on train-accuracy and 0.16 on val-accuracy.
I'm trying to figure out why model's Loss value is always 0.0, so the accuracy seems to be constant as well (which is incorrect in my case, afaik).
Code snippet:
model = Sequential()
model.add(Embedding(vocab_size, glove_vectors.vector_size, weights=[embedding_matrix], input_length=X.shape[1]))
model.add(Dropout(0.5))
model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation="sigmoid"))
model.compile(loss='categorical_crossentropy', optimizer="adam", metrics=["accuracy"])
model.summary()
EPOCHS = 20
train_data, test_data, train_labels, test_labels = train_test_split(X, Y, test_size=0.20, random_state = 42)
print(train_data.shape, train_labels.shape)
print(test_data.shape, test_labels.shape)
val_data = (test_data, test_labels)
history = model.fit(train_data, train_labels, validation_data=val_data, epochs=EPOCHS)
score = model.evaluate(test_data, test_labels)
Output:
Epoch 1/20
25/25 [==============================] - 4s 69ms/step - loss: 0.0000e+00 - accuracy: 0.5241 - val_loss: 0.0000e+00 - val_accuracy: 0.4650
Epoch 2/20
25/25 [==============================] - 1s 55ms/step - loss: 0.0000e+00 - accuracy: 0.4927 - val_loss: 0.0000e+00 - val_accuracy: 0.4650
Epoch 3/20
25/25 [==============================] - 1s 55ms/step - loss: 0.0000e+00 - accuracy: 0.5110 - val_loss: 0.0000e+00 - val_accuracy: 0.4650
Epoch 4/20
25/25 [==============================] - 1s 56ms/step - loss: 0.0000e+00 - accuracy: 0.5074 - val_loss: 0.0000e+00 - val_accuracy: 0.4650
Epoch 5/20
25/25 [==============================] - 1s 55ms/step - loss: 0.0000e+00 - accuracy: 0.5363 - val_loss: 0.0000e+00 - val_accuracy: 0.4650
Epoch 6/20
25/25 [==============================] - 1s 53ms/step - loss: 0.0000e+00 - accuracy: 0.5042 - val_loss: 0.0000e+00 - val_accuracy: 0.4650
Epoch 7/20
25/25 [==============================] - 1s 54ms/step - loss: 0.0000e+00 - accuracy: 0.4904 - val_loss: 0.0000e+00 - val_accuracy: 0.4650
In binary classification there will be 1 node in the output layer even though we will be predicting between two classes. In order to get the output in a probability format between 0 and 1 we will use the sigmoid function.
Hence binary_crossentropy is the correct loss function in your case
model.compile(loss='binary_crossentropy', optimizer="adam", metrics=["accuracy"])
I am working on a image classification project and my model doesn't seem to train properly.
My dataset is made of 4000 images each with a shape of (120,120,3).
Test set represents 20% of the total dataset.
All images have been correctly labeled.
The images are normalized and one-hot encoded. For now I use only two targets, but I will add one more one I start getting decent results.
I use a batch size of 16
I want to use a CNN model.
My current model :
model = keras.models.Sequential()
model.add(Conv2D(filters=16, kernel_size=(6,6), input_shape=(IMG_SIZE,IMG_SIZE,3), activation='relu',))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu',))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(filters=64, kernel_size=(4,4), activation='relu',))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(filters=128, kernel_size=(3,3), activation='relu',))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(filters=256, kernel_size=(2,2), activation='relu',))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
model.compile(optimizer='nadam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
model summary gives :
Total params: 273,330
Trainable params: 273,330
Non-trainable params: 0
from tensorflow.keras.callbacks import EarlyStopping
early_stop = EarlyStopping(monitor='val_loss',patience=10)
history = model.fit(x_train_sample, y_train_sample,
batch_size = BATCH_SIZE,
epochs = EPOCHS,
verbose = 1,
validation_data = (x_test, y_test)
,callbacks=[early_stop,PlotLossesKeras()])
When I run my model for 30 epochs, earlystopping triggers.
Epoch 1/30
43/43 [==============================] - 9s 205ms/step - loss: 0.1109 - accuracy: 0.9531 - val_loss: 0.5259 - val_accuracy: 0.8397
Epoch 2/30
43/43 [==============================] - 10s 231ms/step - loss: 0.0812 - accuracy: 0.9692 - val_loss: 0.5793 - val_accuracy: 0.8355
Epoch 3/30
43/43 [==============================] - 9s 219ms/step - loss: 0.1000 - accuracy: 0.9721 - val_loss: 0.5367 - val_accuracy: 0.8547
Epoch 4/30
43/43 [==============================] - 9s 209ms/step - loss: 0.0694 - accuracy: 0.9707 - val_loss: 0.6101 - val_accuracy: 0.8269
Epoch 5/30
43/43 [==============================] - 9s 203ms/step - loss: 0.0891 - accuracy: 0.9633 - val_loss: 0.6116 - val_accuracy: 0.8419
Epoch 6/30
43/43 [==============================] - 9s 210ms/step - loss: 0.0567 - accuracy: 0.9765 - val_loss: 0.4833 - val_accuracy: 0.8419
Epoch 7/30
43/43 [==============================] - 9s 218ms/step - loss: 0.0312 - accuracy: 0.9897 - val_loss: 1.4513 - val_accuracy: 0.8034
Epoch 8/30
43/43 [==============================] - 9s 213ms/step - loss: 0.0820 - accuracy: 0.9707 - val_loss: 0.5821 - val_accuracy: 0.8248
Epoch 9/30
43/43 [==============================] - 9s 222ms/step - loss: 0.0513 - accuracy: 0.9897 - val_loss: 0.8516 - val_accuracy: 0.8462
Epoch 10/30
43/43 [==============================] - 11s 246ms/step - loss: 0.0442 - accuracy: 0.9853 - val_loss: 0.7927 - val_accuracy: 0.8397
Epoch 11/30
43/43 [==============================] - 10s 222ms/step - loss: 0.0356 - accuracy: 0.9897 - val_loss: 0.7730 - val_accuracy: 0.8141
Epoch 12/30
43/43 [==============================] - 10s 232ms/step - loss: 0.0309 - accuracy: 0.9824 - val_loss: 0.9528 - val_accuracy: 0.8226
Epoch 13/30
43/43 [==============================] - 9s 220ms/step - loss: 0.0424 - accuracy: 0.9839 - val_loss: 1.2109 - val_accuracy: 0.8013
Epoch 14/30
43/43 [==============================] - 10s 228ms/step - loss: 0.0645 - accuracy: 0.9824 - val_loss: 0.5308 - val_accuracy: 0.8547
Epoch 15/30
43/43 [==============================] - 11s 259ms/step - loss: 0.0293 - accuracy: 0.9927 - val_loss: 0.9271 - val_accuracy: 0.8333
Epoch 16/30
43/43 [==============================] - 9s 217ms/step - loss: 0.0430 - accuracy: 0.9795 - val_loss: 0.6687 - val_accuracy: 0.8483
I have tried many different model architectures, changing number of layers, kernel size etc... I can't seem to figure out what is going wrong.
There are many possible reasons.
For starters, depending on your categories, you might want to consider using transfer learning to speed up your training process.
Your architecture looks reasonable and the training and validation loss seems right as well (overfitting is occurring).
Given that you've stated that you could have 3 categories and am currently only using 2, might there be a different distribution between your training set and your test set? That might be causing the model to be unable to generalise well.
For instance, your dataset contains of evenly distributed number of images of Cats, Dogs and Humans. You set 2 categories to train on and thus your model attempts to segment between humans and animals when it tries to validate, there is an uneven distribution in the training data causing the model to see insufficient training size of humans (33%)?
Value of val_acc does not change over the epochs.
Summary:
I'm using a pre-trained (ImageNet) VGG16 from Keras;
from keras.applications import VGG16
conv_base = VGG16(weights='imagenet', include_top=True, input_shape=(224, 224, 3))
Database from ISBI 2016 (ISIC) - which is a set of 900 images of skin lesion used for binary classification (malignant or benign) for training and validation, plus 379 images for testing -;
I use the top dense layers of VGG16 except the last one (that classifies over 1000 classes), and use a binary output with sigmoid function activation;
conv_base.layers.pop() # Remove last one
conv_base.trainable = False
model = models.Sequential()
model.add(conv_base)
model.add(layers.Dense(1, activation='sigmoid'))
Unlock the dense layers setting them to trainable;
Fetch the data, which are in two different folders, one named "malignant" and the other "benign", within the "training data" folder;
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
folder = 'ISBI2016_ISIC_Part3_Training_Data'
batch_size = 20
full_datagen = ImageDataGenerator(
rescale=1./255,
#rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
validation_split = 0.2, # 20% validation
horizontal_flip=True)
train_generator = full_datagen.flow_from_directory( # Found 721 images belonging to 2 classes.
folder,
target_size=(224, 224),
batch_size=batch_size,
subset = 'training',
class_mode='binary')
validation_generator = full_datagen.flow_from_directory( # Found 179 images belonging to 2 classes.
folder,
target_size=(224, 224),
batch_size=batch_size,
subset = 'validation',
shuffle=False,
class_mode='binary')
model.compile(loss='binary_crossentropy',
optimizer=optimizers.SGD(lr=0.001), # High learning rate
metrics=['accuracy'])
history = model.fit_generator(
train_generator,
steps_per_epoch=721 // batch_size+1,
epochs=20,
validation_data=validation_generator,
validation_steps=180 // batch_size+1,
)
Then I fine-tune it with 100 more epochs and lower learning rate, setting the last convolutional layer to trainable.
I've tried many things such as:
Changing the optimizer (RMSprop, Adam and SGD);
Removing the top dense layers of the pre-trained VGG16 and adding mine;
model.add(layers.Flatten())
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
Shuffle=True in validation_generator;
Changing batch size;
Varying the learning rate (0.001, 0.0001, 2e-5).
The results are similar to the following:
Epoch 1/100
37/37 [==============================] - 33s 900ms/step - loss: 0.6394 - acc: 0.7857 - val_loss: 0.6343 - val_acc: 0.8101
Epoch 2/100
37/37 [==============================] - 30s 819ms/step - loss: 0.6342 - acc: 0.8107 - val_loss: 0.6342 - val_acc: 0.8101
Epoch 3/100
37/37 [==============================] - 30s 822ms/step - loss: 0.6324 - acc: 0.8188 - val_loss: 0.6341 - val_acc: 0.8101
Epoch 4/100
37/37 [==============================] - 31s 840ms/step - loss: 0.6346 - acc: 0.8080 - val_loss: 0.6341 - val_acc: 0.8101
Epoch 5/100
37/37 [==============================] - 31s 833ms/step - loss: 0.6395 - acc: 0.7843 - val_loss: 0.6341 - val_acc: 0.8101
Epoch 6/100
37/37 [==============================] - 31s 829ms/step - loss: 0.6334 - acc: 0.8134 - val_loss: 0.6340 - val_acc: 0.8101
Epoch 7/100
37/37 [==============================] - 31s 834ms/step - loss: 0.6334 - acc: 0.8134 - val_loss: 0.6340 - val_acc: 0.8101
Epoch 8/100
37/37 [==============================] - 31s 829ms/step - loss: 0.6342 - acc: 0.8093 - val_loss: 0.6339 - val_acc: 0.8101
Epoch 9/100
37/37 [==============================] - 31s 849ms/step - loss: 0.6330 - acc: 0.8147 - val_loss: 0.6339 - val_acc: 0.8101
Epoch 10/100
37/37 [==============================] - 30s 812ms/step - loss: 0.6332 - acc: 0.8134 - val_loss: 0.6338 - val_acc: 0.8101
Epoch 11/100
37/37 [==============================] - 31s 839ms/step - loss: 0.6338 - acc: 0.8107 - val_loss: 0.6338 - val_acc: 0.8101
Epoch 12/100
37/37 [==============================] - 30s 807ms/step - loss: 0.6334 - acc: 0.8120 - val_loss: 0.6337 - val_acc: 0.8101
Epoch 13/100
37/37 [==============================] - 32s 852ms/step - loss: 0.6334 - acc: 0.8120 - val_loss: 0.6337 - val_acc: 0.8101
Epoch 14/100
37/37 [==============================] - 31s 826ms/step - loss: 0.6330 - acc: 0.8134 - val_loss: 0.6336 - val_acc: 0.8101
Epoch 15/100
37/37 [==============================] - 32s 854ms/step - loss: 0.6335 - acc: 0.8107 - val_loss: 0.6336 - val_acc: 0.8101
And goes on the same way, with constant val_acc = 0.8101.
When I use the test set after finishing training, the confusion matrix gives me 100% correct on benign lesions (304) and 0% on malignant, as so:
Confusion Matrix
[[304 0]
[ 75 0]]
What could I be doing wrong?
Thank you.
VGG16 was trained on RGB centered data. Your ImageDataGenerator does not enable featurewise_center, however, so you're feeding your net with raw RGB data. The VGG convolutional base can't process this to provide any meaningful information, so your net ends up universally guessing the more common class.
In general, when you see this type of problem (your net exclusively guessing the most common class), it means that there's something wrong with your data, not with the net. It can be caused by a preprocessing step like this or by a significant portion of "poisoned" anomalous training data that actively harms the training process.
I'm new to Keras and I'm using it to build a normal Neural Network to classify number MNIST dataset.
Beforehand I have already split the data into 3 parts: 55000 to train, 5000 to evaluate and 10000 to test, and I have scaled the pixel density down (by dividing it by 255.0)
My model looks like this:
model = keras.models.Sequential()
model.add(keras.layers.Flatten(input_shape=[28,28]))
model.add(keras.layers.Dense(100, activation='relu'))
model.add(keras.layers.Dense(10, activation='softmax'))
And here is the compile:
model.compile(loss='sparse_categorical_crossentropy',
optimizer = 'Adam',
metrics=['accuracy'])
I train the model:
his = model.fit(xTrain, yTrain, epochs = 20, validation_data=(xValid, yValid))
At first the val_loss decreases, then it increases although the accuracy is increasing.
Train on 55000 samples, validate on 5000 samples
Epoch 1/20
55000/55000 [==============================] - 5s 91us/sample - loss: 0.2822 - accuracy: 0.9199 - val_loss: 0.1471 - val_accuracy: 0.9588
Epoch 2/20
55000/55000 [==============================] - 5s 82us/sample - loss: 0.1274 - accuracy: 0.9626 - val_loss: 0.1011 - val_accuracy: 0.9710
Epoch 3/20
55000/55000 [==============================] - 5s 83us/sample - loss: 0.0899 - accuracy: 0.9734 - val_loss: 0.0939 - val_accuracy: 0.9742
Epoch 4/20
55000/55000 [==============================] - 5s 84us/sample - loss: 0.0674 - accuracy: 0.9796 - val_loss: 0.0760 - val_accuracy: 0.9770
Epoch 5/20
55000/55000 [==============================] - 5s 94us/sample - loss: 0.0541 - accuracy: 0.9836 - val_loss: 0.0842 - val_accuracy: 0.9742
Epoch 15/20
55000/55000 [==============================] - 4s 82us/sample - loss: 0.0103 - accuracy: 0.9967 - val_loss: 0.0963 - val_accuracy: 0.9788
Epoch 16/20
55000/55000 [==============================] - 5s 84us/sample - loss: 0.0092 - accuracy: 0.9973 - val_loss: 0.0956 - val_accuracy: 0.9774
Epoch 17/20
55000/55000 [==============================] - 5s 82us/sample - loss: 0.0081 - accuracy: 0.9977 - val_loss: 0.0977 - val_accuracy: 0.9770
Epoch 18/20
55000/55000 [==============================] - 5s 85us/sample - loss: 0.0076 - accuracy: 0.9977 - val_loss: 0.1057 - val_accuracy: 0.9760
Epoch 19/20
55000/55000 [==============================] - 5s 83us/sample - loss: 0.0063 - accuracy: 0.9980 - val_loss: 0.1108 - val_accuracy: 0.9774
Epoch 20/20
55000/55000 [==============================] - 5s 85us/sample - loss: 0.0066 - accuracy: 0.9980 - val_loss: 0.1056 - val_accuracy: 0.9768
And when I evaluate the loss is too high:
model.evaluate(xTest, yTest)
Result:
10000/10000 [==============================] - 0s 41us/sample - loss: 25.7150 - accuracy: 0.9740
[25.714989705941953, 0.974]
Is this ok, or is it a sign of overfitting? Should I do something to improve it? Thanks in advance.
Usually, it is not Ok. You want the loss rate to be as small as possible. Your result is typical for overfitting. Your Network 'knows' its training data, but isn't capable of analysing new Images. You may want to add some layers. Maybe Convolutional Layers, Dropout Layer... another idea would be to augment your training images. The ImageDataGenerator-Class provided by Keras might help you out here
Another thing to look at could be your hyperparameters. Why do you use 100 nodes in the first dense layer? maybe something like 784 (28*28) seems more interesting if you want to start with a dense layer. I would suggest some combination of Convolutional-Dropout-Dense. Then your dense -layer maybe doesn't need that many nodes...