Getting wrong prediction for cnn (Dogs Vs Cat ) Keras - python-3.x

Question

I have programmed convolutional neural network in keras to predict whether the image is of a cat or a dog.
I got an accuracy around 80%.
I tried checking the prediction of my code for many images as it always gave result of the one class out of the classes (dogs and cat) to be predicted.
I will walk you through my code:
The first part of my code is Data Preparation:
#Labels
Y = np.concatenate([np.array([[1,0]]*len(cats)),
np.array([[0,1]]*len(dogs))])
print(Y)
#Features
import scipy.misc
from keras.models import model_from_json
X=[]
for name in cats:
converted_image = scipy.misc.imresize(scipy.misc.imread(name), (64, 64))
X.append(converted_image)
for name in dogs:
converted_image = scipy.misc.imresize(scipy.misc.imread(name), (64, 64))
X.append(converted_image)
X= np.array(X)
print(len(X))
print(X.shape)
Now I have divided the data into training and testing
from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.10,random_state=10)
X_train=X_train.astype("float32")
X_test = X_test.astype("float32")
print("Shape Of the Training Features are :",X_train.shape)
print("Shape Of Testing Features are :",X_test.shape)
print("Number of Training Samples : ",X_train.shape[0])
print("Number of Testing Samples :",X_test.shape[0])
Now, I will show my complete CNN architecture below:
from keras.models import Sequential
from keras.layers import Dense,Dropout,Activation,Lambda,Flatten
from keras.layers import Conv2D,MaxPooling2D
def layer(input_shape = (64,64,3)):
model=Sequential()
model.add(Lambda(lambda x: x/127.5 - 1.,input_shape=input_shape, output_shape=input_shape))
model.add(Conv2D(32, (3, 3), activation='relu', name='conv1', input_shape=input_shape, padding="valid"))
model.add(Conv2D(64,(3,3),activation='relu',name='conv2',padding='valid'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(128,(3,3),activation='relu',name='conv3',padding='valid'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
#model.add(Dense(25088,activation='relu'))
#model.add(Dense(12000,activation='relu'))
#model.add(Dense(6000,activation='relu'))
model.add(Dense(500,activation='relu'))
model.add(Dense(2,activation='softmax'))
model.summary()
return model
model = layer()
model.compile(loss='mse',optimizer='adadelta',metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=128, epochs=5, verbose=1, validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
My accuracy and score from the output is as follows:
Test score: 0.1459069953918457
Test accuracy: 0.7876
-Saving and loading The Model Code
#saving
model_json = model.to_json()
open('cd.json','w').write(model_json)
#Save Weights
model.save_weights('cd_weights.h5',overwrite=True)
#loading
model_architecture = 'cd.json'
model_weights = 'cd_weights.h5'
model = model_from_json(open(model_architecture).read())
model.load_weights(model_weights)
Now prediction part
img_names = ['d1.jpg','c1.jpg']
imgs = [np.transpose(scipy.misc.imresize(scipy.misc.imread(img_name), (64, 64)),
(1, 0, 2)).astype('float32')
for img_name in img_names]
imgs = np.array(imgs) / 255
# train
optim = SGD()
model.compile(loss='categorical_crossentropy', optimizer=optim,
metrics=['accuracy'])
predictions = model.predict_classes(imgs)
print(predictions)
Output : [0 0]
The images are taken from training set c1-cat, d1-dog for every image it outputs zero as predicted.

Answer 1

I think there is some problem with model.predict_classes() function.
You can use the following code to perform the same thing as model.predict_classes() can do.
This will give us the prediction in terms of probability.
predictions = model.predict(imgs)
ex:
1) [0.7865448 0.21910465] says there is 78% chance of the picture being the first class (cat) and 21% of chance being the second class(dog)
2) [0.7856076 0.22000787] says there is 78% chance of the picture being the first class (cat) and 21% of chance being the second class(dog)
In order to convert this probability to classes we use np.argmax function.
print(np.argmax(predictions ))
Your output will be 0 indication first class(cat)
or 1 indication second class(dog)