gender.py

from keras.optimizers import Adam
model = tf.keras.models.Sequential([
    # 1st conv
  tf.keras.layers.Conv2D(96, (11,11),strides=(4,4), activation='relu', input_shape=(64, 64, 3)),
  tf.keras.layers.BatchNormalization(),
  tf.keras.layers.MaxPooling2D(2, strides=(2,2)),
    # 2nd conv
  tf.keras.layers.Conv2D(256, (11,11),strides=(1,1), activation='relu',padding="same"),
  tf.keras.layers.BatchNormalization(),
     # 3rd conv
  tf.keras.layers.Conv2D(384, (3,3),strides=(1,1), activation='relu',padding="same"),
  tf.keras.layers.BatchNormalization(),
    # 4th conv
  tf.keras.layers.Conv2D(384, (3,3),strides=(1,1), activation='relu',padding="same"),
  tf.keras.layers.BatchNormalization(),
    # 5th Conv
  tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), activation='relu',padding="same"),
  tf.keras.layers.BatchNormalization(),
  tf.keras.layers.MaxPooling2D(2, strides=(2, 2)),
  # To Flatten layer
  tf.keras.layers.Flatten(),
  # To FC layer 1
  tf.keras.layers.Dense(4096, activation='relu'),
  tf.keras.layers.Dropout(0.5),
  #To FC layer 2
  tf.keras.layers.Dense(4096, activation='relu'),
  tf.keras.layers.Dropout(0.5),
  tf.keras.layers.Dense(1, activation='sigmoid')
  ])
model.compile(
    optimizer=Adam(lr=0.001),
    loss='binary_crossentropy',
    metrics=['accuracy']
   )
hist = model.fit_generator(generator=train_generator,
                    validation_data=validation_generator,
                    steps_per_epoch=256,
                    validation_steps=256,
                    epochs=50)