I am working on an image classification problem where I have to deal with input images of 19 channels. I was successful in doing it with normal 3 channel or greyscale images but I am getting the following error when I changed my code and model to be trained upon 19 channeled inputs.
could not broadcast input array from shape (19,761,3) into shape (19,761,1227,3)
This is my code
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
# dimensions of our images.
img_width, img_height = 761, 1227
train_data_dir = '/home/spectrograms/train'
validation_data_dir = '/home/spectrograms/test'
nb_train_samples = 814
nb_validation_samples = 134
epochs = 50
batch_size = 32
if K.image_data_format() == 'channels_first':
input_shape = (19, img_width, img_height)
else:
input_shape = (img_width, img_height,19)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), input_shape=(19, img_width, img_height), border_mode='same'))
model.add(Activation('relu'))
model.add(Conv2D(32, kernel_size=(3, 3), border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(3, 3), border_mode='same'))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=(3, 3), border_mode='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, kernel_size=(3, 3), border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(3, 3), border_mode='same'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(32, activation='relu'))
model.add(Dense(16, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0,
zoom_range=0,
horizontal_flip=False)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(19,img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(19,img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save_weights('/home/rahul/Roshan/CNN/Saved_models_13/custom/CNN_model.h5')
Related
I am training a CNN model where,
Training data=687 , validation data=102 , testing data=79
The validation accuracy is higher than training accuracy
The test accuracy is very low compared to both validation accuracy and training accuracy.
Validation loss is lower than training loss.
code snippet:
train_datagen = ImageDataGenerator(
rescale=1./255,
# rotation_range=30,
zoom_range=0.1,
horizontal_flip=True,
# vertical_flip=True,
# fill_mode='nearest',
validation_split=.15) # set validation split
val_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.15)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(height, width),
batch_size=batch_size,
class_mode='categorical',
seed=13
)
validation_generator = val_datagen.flow_from_directory(
train_dir, # same directory as training data
target_size=(height, width),
batch_size=batch_size,
class_mode='categorical',
seed=13,
subset='validation'
)
model = Sequential()
model.add(Conv2D(16,3,padding="same", activation="relu", input_shape=(height, width, 3)))
model.add(AveragePooling2D(strides=(2,2), padding="same"))
# model.add(Dropout(0.2))
model.add(Conv2D(32,3,padding="same", activation="relu"))
model.add(AveragePooling2D(strides=(2,2), padding="same"))
# model.add(Dropout(0.2))
model.add(Conv2D(32, 3, padding="same", activation="relu"))
model.add(AveragePooling2D(strides=(2,2), padding="same"))
# model.add(Dropout(0.2))
# model.add(Conv2D(32, 3, padding="same", activation="relu", kernel_regularizer=l2(0.0001)))
# model.add(AveragePooling2D(strides=(2,2), padding="same"))
# model.add(Dropout(0.4))
model.add(Conv2D(64, 3, padding="same", activation="relu"))
model.add(AveragePooling2D(strides=(2,2), padding="same",))
# model.add(Dropout(0.2))
model.add(Conv2D(64, 3, padding="same", activation="relu"))
model.add(AveragePooling2D(strides=(2,2), padding="same"))
# model.add(Dropout(0.2))
model.add(Conv2D(128, 3, padding="same", activation="relu"))
model.add(AveragePooling2D(strides=(2,2), padding="same"))
# model.add(Dropout(0.2))
model.add(Conv2D(128, 3, padding="same", activation="relu"))
model.add(AveragePooling2D(strides=(2,2), padding="same"))
# model.add(Dropout(0.2))
model.add(Conv2D(256, 3, padding="same", activation="relu"))
model.add(AveragePooling2D(strides=(2,2), padding="same"))
# model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(256,activation="relu"))
model.add(Dropout(.5))
# model.add(Dense(256,activation="relu"))
# model.add(Dropout(.5))
model.add(Dense(4, activation="softmax"))
model.summary()
Adam(learning_rate=0.0001, name='Adam')
model.compile(optimizer = 'Adam',loss = 'categorical_crossentropy',metrics = ['accuracy'])
I've done few things to solve this problem:
I didn't used dropout layers between the conv layers
I decreased the range of the data augmentation.
I trained for longer period(the testing accuracy drops to 62% and the val_acc eventually reaches 100%).
What could be the cause of this issue and how can it be resolved?
How can I display test images in Python that have high levels of inaccuracy?
You need to add subset='training' in train_generator. Right now, you are training on both training and validation data.
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(height, width),
batch_size=batch_size,
class_mode='categorical',
seed=13,
subset='training'
)
I have a dataset of images ( EEG spectrograms ) as given below
Image dimensions are 669X1026. I am using the following code for binary classification of the spectrograms.
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
# dimensions of our images.
img_width, img_height = 669, 1026
train_data_dir = '/home/spectrograms/train'
validation_data_dir = '/home/spectrograms/test'
nb_train_samples = 791
nb_validation_samples = 198
epochs = 100
batch_size = 3
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height,3)
model = Sequential()
model.add(Conv2D(128, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(512, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(16))
model.add(Activation('relu'))
# model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0,
zoom_range=0,
horizontal_flip=False)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save_weights('CNN_model.h5')
But I am not able to get a training accuracy greater than 0.53. I have only a limited amount of data ( 790 training samples and 198 testing samples ). So increasing number of input images is not an option. What else can I do to improve the accuracy?
your code
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0,
zoom_range=0,
horizontal_flip=False)
Is not doing any image augmentation, only rescalling. Not sure what type of augmentation may help. Looks like your images really do not rely on color. It probably will not help accuracy but you could reduce computational expense by converting the images to gray scale. You might get some improvement by using the Keras callbacks ReduceLROnPlateau and EarlyStopping. Documentation is here. My suggested code for these callbacks is shown below
rlronp=tf.keras.callbacks.ReduceLROnPlateau(monitor="val_loss", factor=0.5, patience=1,
verbose=1, mode="auto", min_delta=0.0001, cooldown=0, min_lr=0)
estop=tf.keras.callbacks.EarlyStopping(monitor="val_loss", min_delta=0,patience=4,
verbose=1, mode="auto", baseline=None, restore_best_weights=True)
callbacks=[rlronp, estop]
You can try using transfer learning. Most of those models are trained on the imagenet dataset which is dis-similar to the type of images you are using but it might be worth a try. I recommend you use the Mobilenet model. Code for that is shown below
base_model=tf.keras.applications.mobilenet.MobileNet( include_top=False,
input_shape=input_shape, pooling='max', weights='imagenet',dropout=.4)
x=base_model.output
x = Dense(64,activation='relu')(x)
x=Dropout(.3, seed=123)(x)
output=Dense(1, activation='sigmoid')(x)
model=Model(inputs=base_model.input, outputs=output)
model.compile(Adamax(lr=.001), loss='binary_crossentropy', metrics=['accuracy'])
use the callbacks referenced above in model.fit You may get a warning the Mobilenet was trained with an image shape of 224 X 224 X 3 but it should still load the imagenet weights and work.
I have a model that I've trained for 75 epochs. I saved the model with model.save(). The code for training is
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, load_model
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
# dimensions of our images.
img_width, img_height = 320, 240
train_data_dir = 'dataset/Training_set'
validation_data_dir = 'dataset/Test_set'
nb_train_samples = 4000 #total
nb_validation_samples = 1000 # total
epochs = 25
batch_size = 10
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=5)
model.save('model1.h5')
How do I restart training? Do I just run this code again? Or do I need to make changes and what are those changes?
I read that post and tried to understand some. I read this here: Loading a trained Keras model and continue training
You can simply load your model with
from keras.models import load_model
model = load_model('model1.h5')
I am trying to create a CNN model in Keras with multiple conv3d to work on cifar10 dataset. But facing the following issue:
ValueError: ('The specified size contains a dimension with value <=
0', (-8000, 256))
Below is my code that I am trying to execute.
from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv3D, MaxPooling3D
from keras.optimizers import SGD
import os
from keras import backend as K
batch_size = 128
num_classes = 10
epochs = 20
learning_rate = 0.01
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
img_rows = x_train.shape[1]
img_cols = x_train.shape[2]
colors = x_train.shape[3]
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1,colors, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1,colors, img_rows, img_cols)
input_shape = (1, colors, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, colors, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, colors, 1)
input_shape = (img_rows, img_cols, colors, 1)
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv3D(32, kernel_size=(3, 3, 3),activation='relu',input_shape=input_shape))
model.add(Conv3D(32, kernel_size=(3, 3, 3),activation='relu'))
model.add(MaxPooling3D(pool_size=(2, 2, 1)))
model.add(Dropout(0.25))
model.add(Conv3D(64, kernel_size=(3, 3, 3),activation='relu'))
model.add(Conv3D(64, kernel_size=(3, 3, 3),activation='relu'))
model.add(MaxPooling3D(pool_size=(2, 2, 1)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
sgd=SGD(lr=learning_rate)
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=sgd,
metrics=['accuracy'])
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
I have tried with single conv3d and it worked but the accuracy was very low. Code snippet as below:
model = Sequential()
model.add(Conv3D(32, kernel_size=(3, 3, 3),activation='relu',input_shape=input_shape))
model.add(MaxPooling3D(pool_size=(2, 2, 1)))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
Problem
The problem is with the color channel: it equals 3 initially and you're applying the convolution with size 3 and padding='valid'. After the very first Conv3D, the output tensor is:
(None, 30, 30, 1, 32)
... and no more convolutions can be applied to that dimension. The trivial example that you provide is working simply because there's just one convolutional layer.
Solution
One option for you is to set padding='same', so that the tensor shape is preserved:
(None, 32, 32, 3, 32)
However, to me the convolution over colors doesn't add a lot of value, so I'd go with this model:
model = Sequential()
model.add(Conv3D(32, kernel_size=(3, 3, 1), activation='relu', input_shape=input_shape))
model.add(Conv3D(32, kernel_size=(3, 3, 1), activation='relu'))
model.add(MaxPooling3D(pool_size=(2, 2, 1)))
model.add(Dropout(0.25))
model.add(Conv3D(64, kernel_size=(3, 3, 1), activation='relu'))
model.add(Conv3D(64, kernel_size=(3, 3, 1), activation='relu'))
model.add(MaxPooling3D(pool_size=(2, 2, 1)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(10, activation='softmax'))
In practice,in convolution layer the dimension is preserved and in pooling layer you can down sample.
The problem is you are losing dimensionality here. So you can set padding same or use 3X3 filters with one channel instead of using 3 channels.
Using Tensorflow, I build a binary classification model:
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
import tensorflow
import glob
from PIL import Image
import numpy as np
img_width, img_height = 28, 28#all MNIST images are of size (28*28)
train_data_dir = '/Binary Classifier/data/train'#train directory generated by train_cla
validation_data_dir = '/Binary Classifier/data/val'#validation directory generated by val_cla
train_samples = 40000
validation_samples = 10000
epochs = 2
batch_size = 512
if K.image_data_format() == 'channels_first':
input_shape = (1, img_width, img_height)
else:
input_shape = (img_width, img_height, 1)
#build a sequential model to train data
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
train_datagen = ImageDataGenerator(#train data generator
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
val_datagen = ImageDataGenerator(rescale=1. / 255)#validation data generator
train_generator = train_datagen.flow_from_directory(#train generator
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = val_datagen.flow_from_directory(#validation generator
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(#fit the generator to train and validate the model
train_generator,
steps_per_epoch=train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=validation_samples // batch_size)
But I got an error saying "ValueError: Error when checking input: expected conv2d_1_input to have shape (28, 28, 1) but got array with shape (28, 28, 3)", and I don't understand where this error comes from. I specifically defines the input shape to be either (28,28,1) or (28,28,1), and all my input data are MNIST digits which should also be size of (28,28,1). How does the generator receive a (28,28,3) array? Any help is appreciated!
The default in ImageDataGenerator's flow_from_directory is to load color images in RGB format, which implies three channels. You want to load images as grayscale (one channel), and you can do this by setting the color_mode parameter in flow_from_directory to grayscale.
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary', color_mode = 'grayscale')
validation_generator = val_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary', color_mode = 'grayscale')