Conv2d input parameter mismatch - keras

Question

I am giving variable size images (all 278 images of different size 139 of each category) input to my cnn model. As a fact that cnn required fixed size images, so from here i got solution for this is to make input_shape=(None,Nonen,1) (for tensorflow backend and gray scale). but this solution doesnot work with flatten layer, so from their only i got solution of using GlobleMaxpooling or Globalaveragepooling. So from uses these facrts i am making a cnn model in keras to train my network with following code:
import os,cv2
import numpy as np
from sklearn.utils import shuffle
from keras import backend as K
from keras.utils import np_utils
from keras.models import Sequential
from keras.optimizers import SGD,RMSprop,adam
from keras.layers import Conv2D, MaxPooling2D,BatchNormalization,GlobalAveragePooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import regularizers
from keras import initializers
from skimage.io import imread_collection
from keras.preprocessing import image
from keras import Input
import keras
from keras import backend as K
#%%
PATH = os.getcwd()
# Define data path
data_path = PATH+'/current_exp'
data_dir_list = os.listdir(data_path)
img_rows=None
img_cols=None
num_channel=1
# Define the number of classes
num_classes = 2
img_data_list=[]
for dataset in data_dir_list:
img_list=os.listdir(data_path+'/'+ dataset)
print ('Loaded the images of dataset-'+'{}\n'.format(dataset))
for img in img_list:
input_img=cv2.imread(data_path + '/'+ dataset + '/'+ img,0)
img_data_list.append(input_img)
img_data = np.array(img_data_list)
if num_channel==1:
if K.image_dim_ordering()=='th':
img_data= np.expand_dims(img_data, axis=1)
print (img_data.shape)
else:
img_data= np.expand_dims(img_data, axis=4)
print (img_data.shape)
else:
if K.image_dim_ordering()=='th':
img_data=np.rollaxis(img_data,3,1)
print (img_data.shape)
#%%
num_classes = 2
#Total 278 sample, 139 for 0 category and 139 for category 1
num_of_samples = img_data.shape[0]
labels = np.ones((num_of_samples,),dtype='int64')
labels[0:138]=0
labels[138:]=1
x,y = shuffle(img_data,labels, random_state=2)
y = keras.utils.to_categorical(y, 2)
model = Sequential()
model.add(Conv2D(32,(2,2),input_shape=(None,None,1),activation='tanh',kernel_initializer=initializers.glorot_uniform(seed=100)))
model.add(Conv2D(32, (2,2),activation='tanh'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (2,2),activation='tanh'))
model.add(Conv2D(64, (2,2),activation='tanh'))
model.add(MaxPooling2D())
model.add(Dropout(0.25))
#model.add(Flatten())
model.add(GlobalAveragePooling2D())
model.add(Dense(256,activation='tanh'))
model.add(Dropout(0.25))
model.add(Dense(2,activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer='rmsprop',metrics=['accuracy'])
model.fit(x, y,batch_size=1,epochs=5,verbose=1)
but i am getting following error:
ValueError: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (278, 1)
how to solve it.

Answer 1

In the docs for Conv2D it says that the input tensor has to be in this format:
(samples, channels, rows, cols)
I believe you can't have a variable input size unless your network is fully convolutional.
Maybe what you want to do is to keep it to a fixed input size, and just resize the image to that size before feeding it into your network?

Answer 2

Your array with input data cannot have variable dimensions (this is a numpy limitation).
So the array, instead of being a regular array of numbers with 4 dimensions is being created as an array of arrays.
You should fit each image individually because of this limitation.
for epoch in range(epochs):
for img,class in zip(x,y):
#expand the first dimension of the image to have a batch size
img = img.reshape((1,) + img.shape)) #print and check there are 4 dimensions, like (1, width, height, 1).
class = class.reshape((1,) + class.shape)) #print and check there are two dimensions, like (1, classes).
model.train_on_batch(img,class,....)