[Python] will dropout layer enhance accuracy

I know that adding a dropout layer into a CNN model enhances accuracy, since it decrease the impact of over-fitting. However, I built a CNN model with 16,32 and 64 filters, size 3 and maxpool of 2 and noticed that the model without the dropout layer performed better than the model with a dropout layer for all cases.

from keras.models import Sequential
from keras.layers import Conv2D,Activation,MaxPooling2D,Dense,Flatten,Dropout
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from IPython.display import display
import matplotlib.pyplot as plt
from PIL import Image
from sklearn.metrics import classification_report, confusion_matrix
import keras
from keras.layers import BatchNormalization
from keras.optimizers import Adam
import pickle

classifier = Sequential()
classifier.add(Conv2D(16,(3,3),input_shape=(200,200,3)))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size =(2,2)))
classifier.add(Flatten())
classifier.add(Dense(128))
classifier.add(Activation('relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(7))
classifier.add(Activation('softmax'))
classifier.summary()
classifier.compile(optimizer =keras.optimizers.Adam(lr=0.001),
loss ='categorical_crossentropy',
metrics =['accuracy'])
train_datagen = ImageDataGenerator(rescale =1./255,
shear_range =0.2,
zoom_range = 0.2,
horizontal_flip =True)
test_datagen = ImageDataGenerator(rescale = 1./255)

batchsize=10
training_set = train_datagen.flow_from_directory('/home/osboxes/Downloads/Downloads/Journal_Paper/Malware_Families/Spectrogram/Train/',
target_size=(200,200),
batch_size= batchsize,
class_mode='categorical')

test_set = test_datagen.flow_from_directory('/home/osboxes/Downloads/Downloads/Journal_Paper/Malware_Families/Spectrogram/Validate/',
target_size = (200,200),
batch_size = batchsize,
shuffle=False,
class_mode ='categorical')
history=classifier.fit_generator(training_set,
steps_per_epoch = 2340 // batchsize,
epochs = 100,
validation_data =test_set,
validation_steps = 781 // batchsize)

classifier.save('16_With_Dropout_rl_001.h5')
with open('16_With_Dropout_rl_001.h5', 'wb') as file_pi:
pickle.dump(history.history, file_pi)
Y_pred = classifier.predict_generator(test_set, steps= 781 // batchsize+1)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(test_set.classes, y_pred))
print('Classification Report')
target_names = test_set.classes
class_labels = list(test_set.class_indices.keys())
target_names = ['coinhive','emotet','fareit','gafgyt','mirai','ramnit','razy']
report = classification_report(test_set.classes, y_pred, target_names=class_labels)
print(report)

# summarize history for accuracy
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy 16 with dropout rl .001')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss 16 with dropout rl .001')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()




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