tfkerastfrecord.py

 dataset = generate(tfrecordfiles)
 
IM_SIZE = 224 # image size
image_input = tf.keras.Input(shape=(IM_SIZE, IM_SIZE, 3), name='input_layer')
# Some convolutional layers
conv_1 = tf.keras.layers.Conv2D(32,
                                kernel_size=(3, 3),
                                padding='same',
                                activation='relu')(image_input)
conv_1 = tf.keras.layers.MaxPooling2D(padding='same')(conv_1)
conv_2 = tf.keras.layers.Conv2D(32,
                                kernel_size=(3, 3),
                                padding='same',
                                activation='relu')(conv_1)
conv_2 = tf.keras.layers.MaxPooling2D(padding='same')(conv_2)

# Flatten the output of the convolutional layers
conv_flat = tf.keras.layers.Flatten()(conv_2)

# Some dense layers with two separate outputs
fc_1 = tf.keras.layers.Dense(128,
    #                             activation='relu')(conv_flat)
fc_1 = tf.keras.layers.Dropout(0.2)(fc_1)
fc_2 = tf.keras.layers.Dense(128,
                             activation='relu')(fc_1)
fc_2 = tf.keras.layers.Dropout(0.2)(fc_2)

# Output layers: separate outputs for the weather and the ground labels
output = tf.keras.layers.Dense(2,
                               activation='softmax',
                               name='weather')(fc_2)

final_model = tf.keras.Model(inputs=image_input, outputs=[output])

final_model.compile(optimizer='adam', 
              loss="categorical_crossentropy",  
              metrics=['accuracy'])


final_model.summary()

print("Training model")
final_model.fit(dataset, epochs=2, steps_per_epoch=10)