My sample CNN code looks below:
classifier = Sequential()
#1st Conv layer
classifier.add(Convolution2D(64, (9, 9), input_shape=(64, 64, 3), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(4,4)))
#2nd Conv layer
classifier.add(Convolution2D(32, (3, 3), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2,2)))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dropout(0.2))
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 2, activation = 'softmax'))
classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
training_set = train_datagen.flow_from_directory('D:/regionGrowing_MLT/png_orig_imgs/Training',
target_size = (64, 64),
batch_size = 32,
class_mode = 'categorical')
test_datagen = ImageDataGenerator(rescale = 1./255)
test_set = test_datagen.flow_from_directory('D:/regionGrowing_MLT/png_orig_imgs/Test',
target_size = (64, 64),
batch_size = 32,
class_mode = 'categorical'
)
probs=classifier.fit(x = training_set, validation_data = test_set, epochs = 50)
I tried the following line to find the ROC curve, but i get an error message:
predictions = classifier.predict(test_set)
fpr, tpr,threshold = roc_curve(test_set,predictions)
The following error message is displayed:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-52-2ea53b1ba7f1> in <module>
----> 1 fpr, tpr,threshold = roc_curve(test_set,predictions)
ValueError: Expected array-like (array or non-string sequence), got <keras.preprocessing.image.DirectoryIterator object at 0x000002D21D1B61C0>
Any suggestions would be appreciated.
Emm! From the error, I think you have to change keras.processing image object to array. Try this I think this will help you out.
Accuracy
fil_acc_orig = accuracy_score(y_test, predictions.to_array())
ROC Curve
fil_acc_orig = roc_curve(y_test, predictions.to_array())
Related
I want to combine four CNN (pop1, pop2, pop3, and pop4) into one in Keras. My goal is to build a classifier able to assign an image to three possible outcomes. Here is how a concatenate the CNNs:
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Concatenate
from keras.models import Model
# Start With Pop1 Branch ############################################################
classifier_pop1 = Sequential()
classifier_pop1.add(Conv2D(1, (3,3), input_shape = (4009, 36, 1), activation = 'relu'))
classifier_pop1.add(MaxPooling2D(pool_size = (3,3)))
classifier_pop1.add(Conv2D(1, (3,3), activation = 'relu'))
classifier_pop1.add(MaxPooling2D(pool_size = (3,3)))
classifier_pop1.add(Flatten())
classifier_pop1.add(Dense(units = 300, activation = 'relu'))
classifier_pop1.add(Dense(units = 24, activation = 'relu'))
# Start With Pop2 Branch ############################################################
classifier_pop2 = Sequential()
classifier_pop2.add(Conv2D(1, (3,3), input_shape = (4009, 36, 1), activation = 'relu'))
classifier_pop2.add(MaxPooling2D(pool_size = (3,3)))
classifier_pop2.add(Conv2D(1, (3,3), activation = 'relu'))
classifier_pop2.add(MaxPooling2D(pool_size = (3,3)))
classifier_pop2.add(Flatten())
classifier_pop2.add(Dense(units = 300, activation = 'relu'))
classifier_pop2.add(Dense(units = 24, activation = 'relu'))
# Start With Pop3 Branch ############################################################
classifier_pop3 = Sequential()
classifier_pop3.add(Conv2D(1, (3,3), input_shape = (4009, 32, 1), activation = 'relu'))
classifier_pop3.add(MaxPooling2D(pool_size = (3,3)))
classifier_pop3.add(Conv2D(1, (3,3), activation = 'relu'))
classifier_pop3.add(MaxPooling2D(pool_size = (3,3)))
classifier_pop3.add(Flatten())
classifier_pop3.add(Dense(units = 300, activation = 'relu'))
classifier_pop3.add(Dense(units = 24, activation = 'relu'))
# Start With Pop4 Branch ############################################################
classifier_pop4 = Sequential()
classifier_ pop4.add(Conv2D(1, (3,3), input_shape = (4009, 18, 1), activation = 'relu'))
classifier_ pop4.add(MaxPooling2D(pool_size = (3,3)))
classifier_ pop4.add(Conv2D(1, (3,3), activation = 'relu'))
classifier_ pop4.add(MaxPooling2D(pool_size = (3,3)))
classifier_ pop4.add(Flatten())
classifier_ pop4.add(Dense(units = 300, activation = 'relu'))
classifier_ pop4.add(Dense(units = 24, activation = 'relu'))
# Making The Combinition ##########################################################
model_concat = Concatenate()([classifier_pop1.output,classifier_pop2.output,classifier_pop3.output,classifier_ pop4.output])
model_concat = Dense(3, activation='softmax')(model_concat)
model = Model(inputs=[classifier_pop1.input,classifier_pop2.input,classifier_pop3.input,classifier_ pop4.input], outputs=model_concat)
Model_plot
## Compiling the model
model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
I suspect that the error in my code has to do with the below code. I have four folders (pop1, pop2, pop3, and pop4) that contain three subfolders inside (Model1, Model2, and Model3). I am not sure how to use imageGenerator in this part. I just followed this other post: Combining Two CNN's
##Image preprocessing
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set_pop1 = train_datagen.flow_from_directory('./Datasets/training_set/Pop1',
target_size = (4009, 36),
batch_size = 100,
class_mode = 'categorical',
color_mode='grayscale',
shuffle=True)
test_set_pop1 = test_datagen.flow_from_directory('./Datasets/test_set/Pop1',
target_size = (4009, 36),
batch_size = 100,
class_mode = 'categorical',
color_mode='grayscale',
shuffle=False)
training_set_pop2 = train_datagen.flow_from_directory('./Datasets/training_set/Pop2',
target_size = (4009, 36),
batch_size = 100,
class_mode = 'categorical',
color_mode='grayscale',
shuffle=True)
test_set_pop2 = test_datagen.flow_from_directory('./Datasets/test_set/Pop2',
target_size = (4009, 36),
batch_size = 100,
class_mode = 'categorical',
color_mode='grayscale',
shuffle=False)
training_set_pop3 = train_datagen.flow_from_directory('./Datasets/training_set/Pop3',
target_size = (4009, 32),
batch_size = 100,
class_mode = 'categorical',
color_mode='grayscale',
shuffle=True)
test_set_pop3 = test_datagen.flow_from_directory('./Datasets/test_set/Pop3',
target_size = (4009, 32),
batch_size = 100,
class_mode = 'categorical',
color_mode='grayscale',
shuffle=False)
training_set_pop4 = train_datagen.flow_from_directory('./Datasets/training_set/Planiceps',
target_size = (4009, 18),
batch_size = 100,
class_mode = 'categorical',
color_mode='grayscale',
shuffle=True)
test_set_pop4 = test_datagen.flow_from_directory('./Datasets/test_set/Planiceps',
target_size = (4009, 18),
batch_size = 100,
class_mode = 'categorical',
color_mode='grayscale',
shuffle=False)
model.fit([training_set_pop1,training_set_pop2,training_set_pop3,training_set_pop4],
steps_per_epoch = int(2400/100),
epochs = 4,
validation_data = [test_set_pop1,test_set_pop2,test_set_pop3,test_set_pop4],
validation_steps = int(600/100))
Then, I got this error:
ValueError: Failed to find data adapter that can handle input: (<class 'list'> containing values of types {"<class 'keras_preprocessing.image.directory_iterator.DirectoryIterator'>"}), <class 'NoneType'>
Maybe you could try the following, since you can't pass the generators as a list:
model.fit(zip(training_set_pop1,training_set_pop2,training_set_pop3,training_set_pop4),
steps_per_epoch = int(2400/100),
epochs = 4,
validation_data = zip(test_set_pop1,test_set_pop2,test_set_pop3,test_set_pop4),
validation_steps = int(600/100))
I am trying to train a model on a dataset of 40x40 grayscale images and I am getting this error:
ValueError: A target array with shape (32, 5) was passed for an output of shape (None, 4) while using as loss categorical_crossentropy. This loss expects targets to have the same shape as the output.
I don't know where the array of (32, 5) coming from as it should be (32, 4), so I didn't know what to change. Any suggestion?
image_generator = ImageDataGenerator(#rescale = 1/255,
shear_range = 0.3,
zoom_range = 0.1,
rotation_range = 30,
width_shift_range = 0.08,
height_shift_range = 0.08,
horizontal_flip = True,
fill_mode = 'nearest',
)
train_image_generator = image_generator.flow_from_directory('/data1/mypath/generated-images/train',
target_size = (40,40),
color_mode = 'grayscale',
batch_size = 32,
class_mode = 'categorical')
test_image_generator = image_generator.flow_from_directory('/data1/mypath/generated-images/test',
target_size = (40,40),
color_mode = 'grayscale',
batch_size = 32,
class_mode = 'categorical',
shuffle = False)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3,3),input_shape=(40,40, 1), activation='relu', padding='same'))
model.add(BatchNormalization(axis=-1))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(32, kernel_size=(3,3),activation='relu', padding='same'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3,3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(4))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
early_stopping = EarlyStopping(monitor='val_loss',patience=5)
model.fit_generator(train_image_generator, epochs=150,
validation_data = test_image_generator,
callbacks=[early_stopping]) ```
Check the number of classes in your dataset.
The array (32,5) is coming from the shape of your dataset.
The shape of Y(label), is showing as 5 classes, and you have declared it as 4 in the last output layer as (None, 4). Which means the code is reading 4 classes from the image path provided.
I have a sample data of page visits of one page for 803 days. I have extracted features from data like mean, median etc and final shape of data is (803, 25). I had taken a train set of 640 and a test set of 160. I am trying to use CNN+LSTM model using Keras. But I am getting an error in model.fit method.
I have tried permute layer and changed input shapes but still not able to fix it.
trainX.shape = (642, 1, 25)
trainY.shape = (642,)
testX.shape = (161, 1, 25)
testY.shape = (161,)
'''python
# Basic layer
model = Sequential()
model.add(TimeDistributed(Convolution2D(filters = 32, kernel_size = (3, 3), strides=1, padding='SAME', input_shape = (642, 25, 1), activation = 'relu')))
model.add(TimeDistributed(Convolution2D(filters = 32, kernel_size = (3, 3), activation = 'relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size = (2, 2))))
model.add(TimeDistributed(Convolution2D(32, 3, 3, activation = 'relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size = (2, 2))))
model.add(TimeDistributed(Flatten()))
model.add(Permute((2, 3), input_shape=(1, 25)))
model.add(LSTM(units=54, return_sequences=True))
# To avoid overfitting
model.add(Dropout(0.2))
# Adding 6 more layers
model.add(LSTM(units=25, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=54))
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(units = 1, activation='relu', kernel_regularizer=regularizers.l1(0.0001))))
model.add(PReLU(weights=None, alpha_initializer="zero")) # add an advanced activation
model.compile(optimizer = 'adam', loss = customSmapeLoss, metrics=['mae'])
model.fit(trainX, trainY, epochs = 50, batch_size = 32)
predictions = model.predict(testX)
'''
#Runtime Error
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-218-86932db86d0b> in <module>()
42
43 model.compile(optimizer = 'adam', loss = customSmapeLoss, metrics=['mae'])
---> 44 model.fit(trainX, trainY, epochs = 50, batch_size = 32)
Error - IndexError: list index out of range
The input_shape requires a tuple of length 4 when it using TimeDistributed with Conv2D
see https://keras.io/layers/wrappers/
input_shape=(10, 299, 299, 3))
Dont you think your data field is too little? usually, CNN+LSTM is meant for a more complicated task with thousands of sequential images/videos.
I am designing a CNN for classification two types of images, and I need to compute the FPR and TPR.
In the following, you can see my code, but I don't know how can I compute FPR and TPR based on this code.
Could you please let me know how can I do that.
I know for computing the FPR and TPR I can use the following code
fpr, tpr, thresholds = metrics.roc_curve(y_test, y_predic)
while y_predict can be computed by y_predic = model.predict(x_test)
but in my code, I don't know how can do that.
I would be so thankful if you could help me.
num_classes = 1
batch_size = 512
epoch = 100
model = Sequential()
model.add(Conv2D(filters=8, kernel_size=(3, 3), padding='Valid', activation='relu', input_shape=(64, 64, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(rate=0.3))
model.add(Conv2D(filters=8, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(Dropout(rate=0.3))
model.add(Conv2D(filters=16, kernel_size=(3, 3), padding='Same',activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(rate=0.3))
model.add(Flatten())
model.add(Dense(units=128, activation='relu'))
model.add(Dense(units=num_classes, activation='sigmoid'))
model.compile(optimizer='adam', loss=keras.losses.binary_crossentropy, metrics=['accuracy'])
print(model.summary())
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('D:/Data/CNN/train',
target_size = (64, 64),
batch_size = batch_size,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('D:/Data/CNN/test',
target_size = (64, 64),
batch_size = batch_size,
class_mode = 'binary', shuffle= True)
history = model.fit_generator(training_set,
steps_per_epoch = 4000//batch_size,
epochs = epoch,
verbose= 2,
validation_data = test_set,
validation_steps = 1000//batch_size)
You can use keras.metrics almost as is. It has both FPR, TPR and SensitivityAtSpecificity
model.compile(optimizer='adam', loss=keras.losses.binary_crossentropy, metrics=['accuracy', tf.keras.metrics.SensitivityAtSpecificity(0.5)])
But keras model supports only keras.metrics, so no tf.metrics could be used without writing custom metric
https://www.tensorflow.org/api_docs/python/tf/keras/metrics
I have done a programm on image classification of two objects namely dogs and cats using CNN in keras. Now how can I increase the number of classes,i.e, dogs, cats, and frog?
Here's the code:
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint
classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
filepath="weights-improvment-{epoch:02d}-{val_acc:.2f}.hdf5"
checpoint=ModelCheckpoint(filepath,monitor='val_acc',verbose=1,save_best_only=True,mode='max')
callback_list=[checpoint]
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 10,
validation_data = test_set,
validation_steps = 2000)
classifier.save('model_after_trained.h5')
In order to classify more than two classes, the number of neurons(units) in the last layer must be changed to the number of classes to be predicted.
Suppose if you want to predict 3 objects, the last layer must be changed as:
classifier.add(Dense(units = 3, activation = 'sigmoid'))
Please find the below link which will help you to do multi-class classification using CNN: https://www.codesofinterest.com/2017/08/bottleneck-features-multi-class-classification-keras.html
Hope this helps!!!