train_model.py

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, Conv2D, MaxPooling2D, GlobalAveragePooling2D

model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(3, 3), input_shape=(100, 100, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
print(model.summary())
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
history = model.fit(x_train, y_train, batch_size=32, epochs=150,
          validation_data=(x_test, y_test),
         verbose=1)