model prediction using CNN - tensorflow

Question

I am currently build an CNN model using Tensor flow -2.0 but not using transfer learning. My question is that how to predict with a new images? I want to load it from my directory and need predictions (classification problem).
My code is given below -
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Conv2D,MaxPool2D,Dropout,Flatten
from tensorflow.keras.callbacks import EarlyStopping
model = Sequential()
model.add(Conv2D(filters = 16,kernel_size = (3,3), input_shape = image_shape, activation = 'relu'))
model.add(MaxPool2D(pool_size = (2,2)))
model.add(Conv2D(filters = 32,kernel_size = (3,3), activation = 'relu'))
model.add(MaxPool2D(pool_size = (2,2)))
model.add(Conv2D(filters = 64,kernel_size = (3,3), activation = 'relu'))
model.add(MaxPool2D(pool_size = (2,2)))
model.add(Flatten())
model.add(Dense(128,activation = 'relu'))
#model.add(Dropout(0.5))
model.add(Dense(1,activation = 'sigmoid'))
model.compile(loss = 'binary_crossentropy',optimizer = 'adam',
metrics = ['accuracy'])
early_stop = EarlyStopping(monitor = 'val_loss',patience = 2)
batch_size = 16
train_image_gen = image_gen.flow_from_directory(train_path,
target_size = image_shape[:2],
color_mode = 'rgb',
batch_size = batch_size,
class_mode = 'binary')
test_image_gen = image_gen.flow_from_directory(test_path,
target_size = image_shape[:2],
color_mode = 'rgb',
batch_size = batch_size,
class_mode = 'binary',
shuffle = False)
class myCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
if(logs.get('accuracy')>0.97):
print("\nReached 97% accuracy so cancelling training!")
self.model.stop_training = True
callbacks = myCallback()
results = model.fit_generator(train_image_gen,epochs = 85,
validation_data = test_image_gen,
callbacks = [callbacks])
# Let's now save our model to a file
model.save('cell_image_classifier.h5')
# Load the model
model = tf.keras.models.load_model('cell_image_classifier.h5')
model.evaluate_generator(test_image_gen)
#Prediction on image
pred = model.predict_generator(test_image_gen)
predictions = pred > .5
print(classification_report(test_image_gen.classes,predictions))
confusion_matrix(test_image_gen.classes,predictions)
Now externally I want to load the image and need prediction.

Answer 1

This will do!
import numpy as np
from keras.preprocessing import image
# predicting images
fn = 'cat-2083492_640.jpg' # name of the image
path='/content/' + fn # path to the image
img=image.load_img(path, target_size=(150, 150)) # edit the target_size
x=image.img_to_array(img)
x=np.expand_dims(x, axis=0)
images = np.vstack([x])
classes = model.predict(images, batch_size=16) # edit the batch_size
print(classes)