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I am new to deep learning and neural network so I need help understanding why this is happening and how i can fix it.
I have a training size of 7500 images
This is my model
img_size = 50
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(img_size, img_size, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.summary()
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(learning_rate=2*1e-4),
metrics=['acc'])
# Date processing
# All images will be rescaled by 1./255
train_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,
horizontal_flip=True,)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
# This is the target directory
train_dir,
target_size=(img_size, img_size),
batch_size=20,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(img_size, img_size),
batch_size=20,
class_mode='binary')
# Train the Model
history = model.fit(
train_generator,
steps_per_epoch=375, #train_sample_size/data_batch_size
epochs=100,
validation_data=validation_generator,
validation_steps=50)
I have tried changing the parameters, such as adding dropout, changing batch size etc.. but still get a really high loss. The loss would be in the negative 20million and just keep increases.
I am using this code for a CNN
train_batches = ImageDataGenerator().flow_from_directory('dice_sklearn/train', target_size=(IMG_WIDTH, IMG_HEIGHT),
classes=['1', '2', '3', '4', '5', '6'],
batch_size=cv_opt['batch'],
color_mode="grayscale")
test_batches = ImageDataGenerator().flow_from_directory('dice_sklearn/test', target_size=(IMG_WIDTH, IMG_HEIGHT),
class_mode='categorical',
batch_size=cv_opt['batch'],
shuffle=False)
train_num = len(train_batches)
test_num = len(test_batches)
model = Sequential([
Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=(IMG_WIDTH, IMG_HEIGHT, 1)),
Conv2D(32, (3, 3), activation='relu'),
MaxPooling2D(pool_size=(2, 2)),
Dropout(0.30),
Conv2D(64, (3, 3), padding='same', activation='relu'),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D(pool_size=(2, 2)),
Dropout(0.30),
Conv2D(64, (3, 3), padding='same', activation='relu'),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D(pool_size=(2, 2)),
Dropout(0.30),
Flatten(),
Dense(512, activation='relu'),
Dropout(0.5),
Dense(6, activation='softmax'),
])
print(model.summary())
model.compile(Adam(lr=cv_opt['lr']), loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(train_batches, steps_per_epoch=train_num,
epochs=cv_opt['epoch'], verbose=2)
model.save('cnn-keras.h5')
test_batches.reset()
prediction = model.predict(test_batches, steps=test_num, verbose=1)
predicted_class = np.argmax(prediction, axis=1)
classes = test_batches.classes[test_batches.index_array]
accuracy = (predicted_class == classes).mean()
print("Final accuracy:", accuracy * 100)
Where
cv_opt['batch'] is set to 50
cv_opt['lr'] is set to 0.0003
cv_opt['epoch'] is set to 50
The output from the training phase (with model.fit) on the last line (last epoch) returns:
192/192 [==============================] - 98s 510ms/step - loss: 0.0514 - accuracy: 0.9818 - val_loss: 0.0369 - val_accuracy: 0.9833
But when I run this part of code:
test_batches.reset()
prediction = model.predict(test_batches, steps=test_num, verbose=1)
predicted_class = np.argmax(prediction, axis=1)
classes = test_batches.classes[test_batches.index_array]
accuracy = (predicted_class == classes).mean()
print("Final accuracy:", accuracy * 100)
I get an accuracy score very very low: (0.16).
But if a plot the learning curves I can see that the test/validation curve (if in testing or in parameter tuning) both reach accuracies near 90%.
Am I using the model.predict in the wrong way?
Your model is not overfitting. Steps 1 and 2 do not have to be implemented at all in order to solve your problem. In fact, it is even more wrong since the author states that in case of overfitting you need to add more layers, which is strongly advised against: when one has an overfitting model, the model needs to be made simpler, not more complex.
The solution to your issue lies in #Dr.Snoopy's answer : the order of the classes do not match.
My recommendation is to iterate manually through the entire test set, get the ground truth, get the prediction (ensure the same exact preprocessing on images like in the training set is applied on your test set images) before you feed them to your model.
Then, calculate your metrics. This will solve your problem.
For example, you could use the idea below:
correctly_predicted = 0
for image in os.scandir(path_to_my_test_directory):
image_path = image.path
image = cv2.imread(image_path)
image = apply_the_same_preprocessing_like_in_training(image)
#transform from (H,W,3) to (1,H,W,3) because TF + Keras predict only on batches
image = np.expand_dims(image,axis=0)
prediction_label = np.argmax(model.predict(image))
if prediction_label == ground_truth_label:
correctly_predicted+=1
I am writing a code for running autoencoder on CIFAR10 dataset and see the reconstructed images.
The requirement is to create
Encoder with First Layer
Input shape: (32,32,3)
Conv2D Layer with 64 Filters of (3,3)
BatchNormalization layer
ReLu activation
2D MaxpoolingLayer with (2,2) filter
Encoder with Second Layer
Conv2D layer with 16 filters (3,3)
BatchNormalization layer
ReLu activation
2D MaxpoolingLayer with (2,2) filter
Final Encoded as MaxPool with (2,2) with all previous layers
Decoder with First Layer
Input shape: encoder output
Conv2D Layer with 16 Filters of (3,3)
BatchNormalization layer
ReLu activation
UpSampling2D with (2,2) filter
Decoder with Second Layer
Conv2D Layer with 32 Filters of (3,3)
BatchNormalization layer
ReLu activation
UpSampling2D with (2,2) filter
Final Decoded as Sigmoid with all previous layers
I understand that
When we are creating Convolutional Autoencoder (or any AE), we need to pass the output of the previous layer to the next layer.
So, when I create the first Conv2D layer with ReLu and then perform BatchNormalization .. in which I pass the Conv2D layer .. right?
But when I do MaxPooling2D .. what should I pass .. BatchNormalization output or Conv2D layer output?
Also, is there any order in which I should be performing these operations?
Conv2D --> BatchNormalization --> MaxPooling2D
OR
Conv2D --> MaxPooling2D --> BatchNormalization
I am attaching my code below ... I have attempted it to two different ways and hence getting different outputs (in terms of model summary and also model training graph)
Can someone please help me by explaining which is the correct method (Method-1 or Method-2)? Also, how do I understand which graph shows better model performance?
Method - 1
input_image = Input(shape=(32, 32, 3))
### Encoder
conv1_1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_image)
bnorm1_1 = BatchNormalization()(conv1_1)
mpool1_1 = MaxPooling2D((2, 2), padding='same')(conv1_1)
conv1_2 = Conv2D(16, (3, 3), activation='relu', padding='same')(mpool1_1)
borm1_2 = BatchNormalization()(conv1_2)
encoder = MaxPooling2D((2, 2), padding='same')(conv1_2)
### Decoder
conv2_1 = Conv2D(16, (3, 3), activation='relu', padding='same')(encoder)
bnorm2_1 = BatchNormalization()(conv2_1)
up1_1 = UpSampling2D((2, 2))(conv2_1)
conv2_2 = Conv2D(32, (3, 3), activation='relu', padding='same')(up1_1)
bnorm2_2 = BatchNormalization()(conv2_2)
up2_1 = UpSampling2D((2, 2))(conv2_2)
decoder = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(up2_1)
model = Model(input_image, decoder)
model.compile(optimizer='adam', loss='binary_crossentropy')
model.summary()
history = model.fit(trainX, trainX,
epochs=50,
batch_size=1000,
shuffle=True,
verbose=2,
validation_data=(testX, testX)
)
As an output of the model summary, I get this
Total params: 18,851
Trainable params: 18,851
Non-trainable params: 0
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.show()
Method - 2
input_image = Input(shape=(32, 32, 3))
### Encoder
x = Conv2D(64, (3, 3), activation='relu', padding='same')(input_image)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = BatchNormalization()(x)
encoder = MaxPooling2D((2, 2), padding='same')(x)
### Decoder
x = Conv2D(16, (3, 3), activation='relu', padding='same')(encoder)
x = BatchNormalization()(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = BatchNormalization()(x)
x = UpSampling2D((2, 2))(x)
decoder = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)
model = Model(input_image, decoder)
model.compile(optimizer='adam', loss='binary_crossentropy')
model.summary()
history = model.fit(trainX, trainX,
epochs=50,
batch_size=1000,
shuffle=True,
verbose=2,
validation_data=(testX, testX)
)
As an output of the model summary, I get this
Total params: 19,363
Trainable params: 19,107
Non-trainable params: 256
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.show()
In method 1, BatchNormalization layers does not exist in the compiled model, as the output of these layers are not used anywhere. You can check this by running model1.summary()
Method 2 is perfectly alright.
Order of the operations :
Conv2D --> BatchNormalization --> MaxPooling2D is usually the common approach.
Though either order would work since, since BatchNorm is just mean and variance normalization.
Edit:
For Conv2D --> BatchNormalization --> MaxPooling2D :
conv1_1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_image)
bnorm1_1 = BatchNormalization()(conv1_1)
mpool1_1 = MaxPooling2D((2, 2), padding='same')(bnorm1_1)
and then use mpool1_1 as input for next layer.
For Conv2D --> MaxPooling2D --> BatchNormalization:
conv1_1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_image)
mpool1_1 = MaxPooling2D((2, 2), padding='same')(conv1_1)
bnorm1_1 = BatchNormalization()(mpool1_1)
and then use bnorm1_1 as input for next layer.
To effectively use BatchNormalization layer, you should always use it before activation.
Instead of:
conv1_1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_image)
bnorm1_1 = BatchNormalization()(conv1_1)
mpool1_1 = MaxPooling2D((2, 2), padding='same')(bnorm1_1)
Use it like this:
conv1_1 = Conv2D(64, (3, 3), padding='same')(input_image)
bnorm1_1 = BatchNormalization()(conv1_1)
act_1 = Activation('relu')(bnorm1_1)
mpool1_1 = MaxPooling2D((2, 2), padding='same')(act_1)
For more details, check here:
Where do I call the BatchNormalization function in Keras?
I trained my model of CNN net on images with good val_acc=0.97 and using model.fit_generator.
Here is the output of last epoch, proofing high validation accuracy:
199/200 [============================>.] - ETA: 1s - loss: 0.1563 - acc: 0.9563
200/200 [==============================] - 306s 2s/step - loss: 0.1556 - acc: 0.9565 - val_loss: 0.1402 - val_acc: 0.9691
Epoch 00005: val_acc improved from 0.96701 to 0.96907, saving model to /home/sergorl/cars/color_weights.hdf5
But when I use the same validation data set, which I use during training, but test only one image and for every image in my validation set I always get the wrong predicted label and the predicted probabilities looks like a uniform distribution.
I read this links:
Wrong prediction on images
Why is Keras training well but returning wrong predictions?
Keras Val_acc is good but prediction for same data is poor
But I don't find the solution!
from keras.models import Sequential,Model,load_model
from keras.optimizers import SGD
from keras.layers import BatchNormalization, Lambda, Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation
from keras.layers.merge import Concatenate
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint
import os
import cv2
import numpy as np
class CarColorNet:
def __init__(self, numClasses=6, imageWidth=256, imageHeight=256):
self.classes = {}
self.numClasses = numClasses
self.imageWidth = imageWidth
self.imageHeight = imageHeight
input_image = Input(shape=(self.imageWidth, self.imageHeight, 3))
# ------------------------------------ TOP BRANCH ------------------------------------
# first top convolution layer
top_conv1 = Convolution2D(filters=48, kernel_size=(11, 11), strides=(4, 4),
input_shape=(self.imageWidth, self.imageHeight, 3), activation='relu')(input_image)
top_conv1 = BatchNormalization()(top_conv1)
top_conv1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(top_conv1)
# second top convolution layer
# split feature map by half
top_top_conv2 = Lambda(lambda x: x[:, :, :, :24])(top_conv1)
top_bot_conv2 = Lambda(lambda x: x[:, :, :, 24:])(top_conv1)
top_top_conv2 = Convolution2D(filters=64, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(top_top_conv2)
top_top_conv2 = BatchNormalization()(top_top_conv2)
top_top_conv2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(top_top_conv2)
top_bot_conv2 = Convolution2D(filters=64, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(top_bot_conv2)
top_bot_conv2 = BatchNormalization()(top_bot_conv2)
top_bot_conv2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(top_bot_conv2)
# third top convolution layer
# concat 2 feature map
top_conv3 = Concatenate()([top_top_conv2, top_bot_conv2])
top_conv3 = Convolution2D(filters=192, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(top_conv3)
# fourth top convolution layer
# split feature map by half
top_top_conv4 = Lambda(lambda x: x[:, :, :, :96])(top_conv3)
top_bot_conv4 = Lambda(lambda x: x[:, :, :, 96:])(top_conv3)
top_top_conv4 = Convolution2D(filters=96, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(top_top_conv4)
top_bot_conv4 = Convolution2D(filters=96, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(top_bot_conv4)
# fifth top convolution layer
top_top_conv5 = Convolution2D(filters=64, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(top_top_conv4)
top_top_conv5 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(top_top_conv5)
top_bot_conv5 = Convolution2D(filters=64, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(top_bot_conv4)
top_bot_conv5 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(top_bot_conv5)
# ------------------------------------ TOP BOTTOM ------------------------------------
# first bottom convolution layer
bottom_conv1 = Convolution2D(filters=48, kernel_size=(11, 11), strides=(4, 4),
input_shape=(224, 224, 3), activation='relu')(input_image)
bottom_conv1 = BatchNormalization()(bottom_conv1)
bottom_conv1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(bottom_conv1)
# second bottom convolution layer
# split feature map by half
bottom_top_conv2 = Lambda(lambda x: x[:, :, :, :24])(bottom_conv1)
bottom_bot_conv2 = Lambda(lambda x: x[:, :, :, 24:])(bottom_conv1)
bottom_top_conv2 = Convolution2D(filters=64, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(bottom_top_conv2)
bottom_top_conv2 = BatchNormalization()(bottom_top_conv2)
bottom_top_conv2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(bottom_top_conv2)
bottom_bot_conv2 = Convolution2D(filters=64, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(bottom_bot_conv2)
bottom_bot_conv2 = BatchNormalization()(bottom_bot_conv2)
bottom_bot_conv2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(bottom_bot_conv2)
# third bottom convolution layer
# concat 2 feature map
bottom_conv3 = Concatenate()([bottom_top_conv2, bottom_bot_conv2])
bottom_conv3 = Convolution2D(filters=192, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(bottom_conv3)
# fourth bottom convolution layer
# split feature map by half
bottom_top_conv4 = Lambda(lambda x: x[:, :, :, :96])(bottom_conv3)
bottom_bot_conv4 = Lambda(lambda x: x[:, :, :, 96:])(bottom_conv3)
bottom_top_conv4 = Convolution2D(filters=96, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(bottom_top_conv4)
bottom_bot_conv4 = Convolution2D(filters=96, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(bottom_bot_conv4)
# fifth bottom convolution layer
bottom_top_conv5 = Convolution2D(filters=64, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(bottom_top_conv4)
bottom_top_conv5 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(bottom_top_conv5)
bottom_bot_conv5 = Convolution2D(filters=64, kernel_size=(3, 3), strides=(1, 1), activation='relu',
padding='same')(bottom_bot_conv4)
bottom_bot_conv5 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(bottom_bot_conv5)
# ---------------------------------- CONCATENATE TOP AND BOTTOM BRANCH ------------------------------------
conv_output = Concatenate()([top_top_conv5, top_bot_conv5, bottom_top_conv5, bottom_bot_conv5])
# Flatten
flatten = Flatten()(conv_output)
# Fully-connected layer
FC_1 = Dense(units=4096, activation='relu')(flatten)
FC_1 = Dropout(0.6)(FC_1)
FC_2 = Dense(units=4096, activation='relu')(FC_1)
FC_2 = Dropout(0.6)(FC_2)
output = Dense(units=self.numClasses, activation='softmax')(FC_2)
self.model = Model(inputs=input_image, outputs=output)
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
# sgd = SGD(lr=0.01, momentum=0.9, decay=0.0005, nesterov=True)
self.model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
def train(self,
pathToTrainSet,
pathToValidSet,
pathToSaveModel,
epochs=7,
batchSize=32,
stepsPerEpoch=200,
validationSteps=1000):
fileOfWeights = 'color_weights.hdf5'
checkpoint = ModelCheckpoint(os.path.join(pathToSaveModel, fileOfWeights),
monitor='val_acc', verbose=1,
save_best_only=True, mode='max')
checkpoint2 = ModelCheckpoint(os.path.join(pathToSaveModel, fileOfWeights),
monitor='val_loss', verbose=1,
save_best_only=True, mode='max')
trainDataGen = ImageDataGenerator(rescale=1.0/255, shear_range=0.2,
zoom_range=0.3, horizontal_flip=True)
validDataGen = ImageDataGenerator(rescale=1.0/255)
trainSet = trainDataGen.flow_from_directory(
pathToTrainSet,
target_size=(self.imageWidth, self.imageHeight),
batch_size=batchSize,
class_mode='categorical'
)
self.classes = {v: k for k, v in trainSet.class_indices.items()}
np.save(os.path.join(pathToSaveModel, 'class_index.npy'), self.classes)
validSet = validDataGen.flow_from_directory(
pathToValidSet,
target_size=(self.imageWidth, self.imageHeight),
batch_size=batchSize,
class_mode='categorical'
)
self.model.fit_generator(
trainSet,
steps_per_epoch=stepsPerEpoch,
epochs=epochs,
validation_data=validSet,
validation_steps=validationSteps//batchSize,
callbacks=[checkpoint, checkpoint2])
print('============================ Saving is here ============================')
self.model.save(os.path.join(pathToSaveModel, 'car_color_net.h5'))
#staticmethod
def load(pathToModel, pathToClassIndexes):
model = load_model(pathToModel)
layers = model.layers
inputShape, outputShape = layers[0].input_shape, layers[-1].output_shape,
imageWidth, imageHeight = inputShape[1], inputShape[2]
numClasses = outputShape[1]
net = CarColorNet(numClasses, imageWidth, imageHeight)
net.classes = np.load(os.path.join(pathToClassIndexes, 'class_index.npy')).item()
return net
def predictOneImage(self, pathToImage):
frame = cv2.imread(pathToImage)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = cv2.resize(frame, (self.imageWidth, self.imageHeight))
frame = np.expand_dims(frame, axis=0)
# cv2.imshow("boxed", frame[0, :, :, :])
# cv2.waitKey(0)
frame = np.asarray(frame, dtype='float32')
img = frame/255
probs = self.model.predict(img)
ind = probs.argmax(axis=-1)[0]
return self.classes[ind]
if __name__ == '__main__':
pathToTrainSet = '/home/sergorl/cars/train'
pathToValidSet = '/home/sergorl/cars/valid'
pathToSaveModel = '/home/sergorl/cars'
## Train net
# net = CarColorNet(numClasses=6)
# net.train(pathToTrainSet, pathToValidSet, pathToSaveModel)
# Test on all images from validSet
net = CarColorNet.load(os.path.join(pathToSaveModel, 'car_color_net.h5'), pathToSaveModel)
count, countTrueLabels = 0, 0
for dirpath, _dirnames, filenames in os.walk(pathToValidSet):
trueLabel = dirpath.split('/')[-1]
for file in filenames:
label = net.predictOneImage(os.path.join(dirpath, file))
print(trueLabel, label)
if label == trueLabel:
countTrueLabels += 1
count += 1
print('rate is {0:.2f}'.format(float(countTrueLabels) / float(count) * 100))
If I have a good val_acc=0.97, I'll expect the same result (or nearly), testing every image in validation set. But always have wrong prediction!
I ran net immediately after train was done and see that learning was good:
if __name__ == '__main__':
pathToTrainSet = '/home/sergorl/cars/train'
pathToValidSet = '/home/sergorl/cars/valid'
pathToSaveModel = '/home/sergorl/cars'
# Train net
net = CarColorNet(numClasses=6)
net.train(pathToTrainSet, pathToValidSet, pathToSaveModel)
# Test on all images from validSet
count, countTrueLabels = 0, 0
for dirpath, _dirnames, filenames in os.walk(pathToValidSet):
trueLabel = dirpath.split('/')[-1]
for file in filenames:
label = net.predictOneImage(os.path.join(dirpath, file))
print(trueLabel, label)
if label == trueLabel:
countTrueLabels += 1
count += 1
print('rate is {0:.2f}'.format(float(countTrueLabels) / float(count) * 100))
So it seems the problem is inside model.save and it looks like saving doesn't work!. I found many related issues on git, for example:
https://github.com/keras-team/keras/issues/4875
https://github.com/keras-team/keras/issues/4904
But I don't know how to fix it with Python 3.7.3 and keras 2.0.0
Can you share more about the issue like what is the output you are getting. From the code i can see that you are training for 6 classes and using categorical cross entropy so ideally you should be getting an array with 6 values with each value bw 0 and 1 and the index of highest value in that array should be the output.
I am taking the images from the SVHN (street view house number dataset, stanford) and I could really use some help in figuring out why my accuracy does not increase past 19%... This is essentially an MNIST tutorial with more difficult images (other numbers could be off center, blurs, shadows etc..)
I essentially take each image and subtract that image's mean then I normalize to 0-1 (divide by 255.)
The pipeline is simple enough:
2 Convolution 2d Layers (32 filters, 3x3)
MaxPool (2x2)
Dropout (.25)
2 Convolution 2d layers (64 filters, 3x3)
Max Pool (2x2)
Dropout(.25)
Flatten
Dense Relu
Dropout(.5)
Dense Softmax (10)
1792/73257 [..............................] - ETA: 3:17 - loss: 2.3241 - acc: 0.1602
1920/73257 [..............................] - ETA: 3:16 - loss: 2.3203 - acc: 0.1625
2048/73257 [..............................] - ETA: 3:14 - loss: 2.3177 - acc: 0.1621
2176/73257 [..............................] - ETA: 3:13 - loss: 2.3104 - acc: 0.1682
...
...
...
53376/73257 [====================>.........] - ETA: 51s - loss: 2.2439 - acc: 0.1879
53504/73257 [====================>.........] - ETA: 51s - loss: 2.2439 - acc: 0.1879
53632/73257 [====================>.........] - ETA: 50s - loss: 2.2439 - acc: 0.1878
53760/73257 [=====================>........] - ETA: 50s - loss: 2.2439 - acc: 0.1879
Can anyone help me figure out what I'm doing wrong? Are there any tips to figuring out why it would increase in the beginning as normal then taper off so quickly?
I am using categorical cross entropy with an rmsprop optimizer
epochs: 20
batch_size: 128
image_size: 32x32
model = Sequential()
model.add(Convolution2D(32, (3, 3),
strides=1,
activation='relu',
padding='same',
input_shape=input_shape,
data_format='channels_last'))
model.add(Convolution2D(32, (3, 3), padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), data_format='channels_last'))
model.add(Dropout(0.25))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(model.output_shape[1], activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
#METHOD1
# print('compiling model...')
# model.compile(loss='mean_squared_error',
# optimizer='sgd',
# metrics=['accuracy'])
# print('fitting model...')
#
# model.fit(X_train, y_train, batch_size=64, epochs=1, verbose=1)
# METHOD2
sgd = SGD(lr=0.05)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
model.fit(X_train, y_train,
epochs=20,
batch_size=128)
score = model.evaluate(X_test, y_test, batch_size=128)