Trjtdtk

I have a pretrained ResNet model which is trained on 64x64 images. I would like to do transfer learning with new dataset that contains 128x128 images.

I am loading the model like:

 train_generator = train_datagen.flow_from_directory(
 train_data_dir,
 target_size=(img_size, img_size),
 batch_size=batch_size,
 subset='training') # set as training data

 validation_generator = train_datagen.flow_from_directory(
 train_data_dir, # same directory as training data
 target_size=(img_size, img_size),
 batch_size=batch_size,
 subset='validation') # set as validation data


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 128x128 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 ;)