Htydjtk

I have a pretrained ResNet model which is trained on 64x64 images. I would like to do transfer learning with new dataset that contains 200x200 images.

I am loading the model like:

model = ResNet50(include_top=False, weights=None, input_shape=(64,64,3))

model.load_weights("a trained model weights on 64x64")



model.layers.pop()

for layer in model.layers:

   layer.trainable = False



x = model.output

x = MaxPooling2D((2,2), strides=(2,2), padding='same')(x)

x = Flatten(name='flatten')(x)

x = Dropout(0.2)(x)

x = Dense(512, activation='relu')(x)

predictions = Dense(101, activation='softmax', name='predictions')(x)



top_model = Model(inputs=model.input, outputs=predictions)



top_model.compile(loss='categorical_crossentropy',

        optimizer=adam,

        metrics=[accuracy])



EPOCHS = 100

BATCH_SIZE = 32

STEPS_PER_EPOCH = 4424 // BATCH_SIZE

VALIDATION_STEPS = 466 // BATCH_SIZE



callbacks = [LearningRateScheduler(schedule=Schedule(EPOCHS, initial_lr=lr_rate)),

                ModelCheckpoint(str(output_dir) + "/weights.{epoch:03d}-{val_loss:.3f}-{val_age_mae:.3f}.hdf5",

                                 monitor="val_age_mae",

                                 verbose=1,

                                 save_best_only=False,

                                 mode="min")

                 ]



hist = top_model.fit_generator(generator=train_set,

                               epochs=EPOCHS,

                               steps_per_epoch = STEPS_PER_EPOCH,

                               validation_data=val_set,

                               validation_steps = VALIDATION_STEPS,

                               verbose=1,

                               callbacks=callbacks)

I would like to do transfer learning based with images of 200x200 pixels. I am very new to this, how can I modify?

is there a way to modify the model input shape? and do I. need to do something with spatial size?

And which optimizer is recommended? Adam or SGD?

__________________________________________________________________________________________________

res5c_branch2a (Conv2D)         (None, 2, 2, 512)    1049088     activation_46[0][0]              

__________________________________________________________________________________________________

bn5c_branch2a (BatchNormalizati (None, 2, 2, 512)    2048        res5c_branch2a[0][0]             

__________________________________________________________________________________________________

activation_47 (Activation)      (None, 2, 2, 512)    0           bn5c_branch2a[0][0]              

__________________________________________________________________________________________________

res5c_branch2b (Conv2D)         (None, 2, 2, 512)    2359808     activation_47[0][0]              

__________________________________________________________________________________________________

bn5c_branch2b (BatchNormalizati (None, 2, 2, 512)    2048        res5c_branch2b[0][0]             

__________________________________________________________________________________________________

activation_48 (Activation)      (None, 2, 2, 512)    0           bn5c_branch2b[0][0]              

__________________________________________________________________________________________________

res5c_branch2c (Conv2D)         (None, 2, 2, 2048)   1050624     activation_48[0][0]              

__________________________________________________________________________________________________

bn5c_branch2c (BatchNormalizati (None, 2, 2, 2048)   8192        res5c_branch2c[0][0]             

__________________________________________________________________________________________________

add_16 (Add)                    (None, 2, 2, 2048)   0           bn5c_branch2c[0][0]              

                                                                 activation_46[0][0]              

__________________________________________________________________________________________________

activation_49 (Activation)      (None, 2, 2, 2048)   0           add_16[0][0]                     

__________________________________________________________________________________________________

pred_age (Dense)                (None, 2, 2, 101)    206848      activation_49[0][0]              

==================================================================================================

Total params: 23,794,560

Trainable params: 23,741,440

Non-trainable params: 53,120

__________________________________________________________________________________________________

Getting the following error

ValueError: Error when checking input: expected input_1 to have shape (64, 64, 3) but got array with shape (128, 128, 3)

you did not mention your generator.

Just add target_size to your train_set generator. it can be as follows.

and your dataset should be in "data_generator" folder, with classes as subfolders.

train_set =ImageDataGenerator(preprocessing_function=preprocess_input)

train_generator= train_set.flow_from_directory('data_generator',

                                                 target_size=(64, 64),

                                                 color_mode='rgb',

                                                 batch_size=32,

                                                 class_mode='categorical',

                                                 shuffle=True)

vote up, if this helps ;)