inp = tf.keras.layers.Input(shape=INPUT_SHAPE)

conv1 = tf.keras.layers.Conv2D(32, kernel_size=(3, 3),
                               activation='relu', padding='same')(inp)
pool1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = tf.keras.layers.Conv2D(64, kernel_size=(3, 3),
                               activation='relu', padding='same')(pool1)
pool2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = tf.keras.layers.Conv2D(128, kernel_size=(3, 3),
                               activation='relu', padding='same')(pool2)
pool3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv3)

flat = tf.keras.layers.Flatten()(pool3)

hidden1 = tf.keras.layers.Dense(512, activation='relu')(flat)
drop1 = tf.keras.layers.Dropout(rate=0.3)(hidden1)
hidden2 = tf.keras.layers.Dense(512, activation='relu')(drop1)
drop2 = tf.keras.layers.Dropout(rate=0.3)(hidden2)

out = tf.keras.layers.Dense(1, activation='sigmoid')(drop2)

model = tf.keras.Model(inputs=inp, outputs=out)
model.compile(optimizer='adam',
                loss='binary_crossentropy',
                metrics=['accuracy'])
model.summary()


# Output
Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         [(None, 125, 125, 3)]     0
_________________________________________________________________
conv2d (Conv2D)              (None, 125, 125, 32)      896
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 62, 62, 32)        0
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 62, 62, 64)        18496
_________________________________________________________________
...
...
_________________________________________________________________
dense_1 (Dense)              (None, 512)               262656
_________________________________________________________________
dropout_1 (Dropout)          (None, 512)               0
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 513
=================================================================
Total params: 15,102,529
Trainable params: 15,102,529
Non-trainable params: 0
_________________________________________________________________