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I am trying to train a pretrained keras model on new data. I came across tensorflow's dataset api and I am trying to use it with my old keras model. I understand that tf data api returns tensors, so the data api as well as model should be part of the same graph and the output of the data api should be connected as input to the model. Here is the code

import tensorflow as tf   

from data_pipeline import ImageDataGenerator

import os

import keras

from keras.engine import InputLayer



os.environ["CUDA_VISIBLE_DEVICES"]="0"

###################### to check visible devices ###############

from tensorflow.python.client import device_lib

print(device_lib.list_local_devices())

###############################################################



_EPOCHS      = 10

_NUM_CLASSES = 2

_BATCH_SIZE  = 32





def training_pipeline():

  # #############

  # Load Dataset

  # #############

  training_set = ImageDataGenerator(directory="\\in-pdc-sem2\training",

                                  horizontal_flip=True, vertical_flip=True, rescale=True, normalize=True,

                                  color_jitter=True, batch_size=_BATCH_SIZE,

                                  num_cpus=8, epochs=60, output_patch_size=389, validation=False).dataset_pipeline()

  testing_set = ImageDataGenerator(directory="\\in-pdc-sem2\training",

                                  horizontal_flip=False, vertical_flip=False, rescale=False, normalize=True,

                                  color_jitter=False, batch_size=_BATCH_SIZE,

                                  num_cpus=8, epochs=60, output_patch_size=389, validation=True).dataset_pipeline()



  print(training_set.output_types, training_set.output_shapes)



  iterator = tf.data.Iterator.from_structure(training_set.output_types, training_set.output_shapes)#((None, 389, 389, 3), (None)))



  train_initializer = iterator.make_initializer(training_set)

  validation_initializer = iterator.make_initializer(testing_set)



  img, labels = iterator.get_next()

  img = img.set_shape((None, 389, 389, 3))



  model = baseline_model(img, labels)  # keras model defined here

  model.summary()



  keras.backend.get_session().run(tf.global_variables_initializer())

  for epoch in range(_EPOCHS):



      # #############

      # Train Model

      # #############

      keras.backend.get_session().run(train_initializer)

      model.fit(

          steps_per_epoch=1000000 // _BATCH_SIZE,

          epochs=1,

          # validation_steps=11970 // _BATCH_SIZE,

          callbacks=callbacks(),

          verbose = 1)



      keras.backend.get_session().run(validation_initializer)



      loss, acc, cross_entropy = model.evaluate(verbose=1, steps=11970 // 32)

      filepath = "./weights/ResNet_16_Best/weights-improvement-Run1-" + str(epoch) + "-" + str(loss) + ".hdf5"

      model.save_weights(filepath, overwrite=True)





def baseline_model(input_tensor, labels):

    jsonFile = '\\in-pdc-sem2\resnetV4_2Best.json'

    weightsFile = '\\in-pdc-sem1\resnetV4_2BestWeightsOnly.hdf5'

    with open(jsonFile, "r") as file:

        jsonDef = file.read()

    from keras.models import model_from_json

    model_single = model_from_json(jsonDef)



    model_single.load_weights(weightsFile)

    model_single.layers[0] = InputLayer(input_tensor=input_tensor, input_shape=(389, 389, 3))

    model_single.compile(target_tensors=[labels], loss='categorical_crossentropy', optimizer='Adam', metrics=[keras.metrics.categorical_accuracy])

    return model_single



def callbacks():

    tensorboard = keras.callbacks.TensorBoard(log_dir='./tensorboard', write_grads=False, write_images=False, histogram_freq=0)

    callbacks_list = [tensorboard]

    return callbacks_list



if __name__ == '__main__':

    training_pipeline()

The "training set" returns image and label tuple, image is a tensor of shape (32, 389, 389, 3), its a batch of 32 images. I verified the shape in a separate script, it is correct. I am defining the input layer of the model using the tensor, and target tensors in the model.compile part.

This is what the model.summary output looks like:

Layer (type)                    Output Shape         Param #     Connected to                     

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

input_1 (InputLayer)            (None, 389, 389, 3)  0                                            

__________________________________________________________________________________________________

conv1 (Conv2D)                  (None, 383, 383, 13) 1924        input_1[0][0]                    

__________________________________________________________________________________________________

bn_conv1 (BatchNormalization)   (None, 383, 383, 13) 52          conv1[0][0]                      

__________________________________________________________________________________________________

activation_1 (Activation)       (None, 383, 383, 13) 0           bn_conv1[0][0]                   

__________________________________________________________________________________________________

max_pooling2d_1 (MaxPooling2D)  (None, 191, 191, 13) 0           activation_1[0][0]               

__________________________________________________________________________________________________

res2a_branch2a (Conv2D)         (None, 191, 191, 4)  56          max_pooling2d_1[0][0]            

__________________________________________________________________________________________________

bn2a_branch2a (BatchNormalizati (None, 191, 191, 4)  16          res2a_branch2a[0][0]             

__________________________________________________________________________________________________

activation_2 (Activation)       (None, 191, 191, 4)  0           bn2a_branch2a[0][0]              

__________________________________________________________________________________________________

res2a_branch2b (Conv2D)         (None, 191, 191, 4)  148         activation_2[0][0]               

__________________________________________________________________________________________________

bn2a_branch2b (BatchNormalizati (None, 191, 191, 4)  16          res2a_branch2b[0][0]             

__________________________________________________________________________________________________

activation_3 (Activation)       (None, 191, 191, 4)  0           bn2a_branch2b[0][0]              

__________________________________________________________________________________________________

res2a_branch2c (Conv2D)         (None, 191, 191, 8)  40          activation_3[0][0]               

__________________________________________________________________________________________________

res2a_branch1 (Conv2D)          (None, 191, 191, 8)  112         max_pooling2d_1[0][0]            

__________________________________________________________________________________________________

bn2a_branch2c (BatchNormalizati (None, 191, 191, 8)  32          res2a_branch2c[0][0]             

__________________________________________________________________________________________________

bn2a_branch1 (BatchNormalizatio (None, 191, 191, 8)  32          res2a_branch1[0][0]              

__________________________________________________________________________________________________

add_1 (Add)                     (None, 191, 191, 8)  0           bn2a_branch2c[0][0]              

                                                                 bn2a_branch1[0][0]               

__________________________________________________________________________________________________

activation_4 (Activation)       (None, 191, 191, 8)  0           add_1[0][0]                      

__________________________________________________________________________________________________

bn2b_branch2a (BatchNormalizati (None, 191, 191, 8)  32          activation_4[0][0]               

__________________________________________________________________________________________________

activation_5 (Activation)       (None, 191, 191, 8)  0           bn2b_branch2a[0][0]              

__________________________________________________________________________________________________

res2b_branch2b (Conv2D)         (None, 191, 191, 4)  292         activation_5[0][0]               

__________________________________________________________________________________________________

bn2b_branch2b (BatchNormalizati (None, 191, 191, 4)  16          res2b_branch2b[0][0]             

__________________________________________________________________________________________________

activation_6 (Activation)       (None, 191, 191, 4)  0           bn2b_branch2b[0][0]              

__________________________________________________________________________________________________

res2b_branch2c (Conv2D)         (None, 191, 191, 8)  40          activation_6[0][0]               

__________________________________________________________________________________________________

add_2 (Add)                     (None, 191, 191, 8)  0           res2b_branch2c[0][0]             

                                                                 activation_4[0][0]               

__________________________________________________________________________________________________

bn2c_branch2a (BatchNormalizati (None, 191, 191, 8)  32          add_2[0][0]                      

__________________________________________________________________________________________________

activation_7 (Activation)       (None, 191, 191, 8)  0           bn2c_branch2a[0][0]              

__________________________________________________________________________________________________

res2c_branch2b (Conv2D)         (None, 191, 191, 4)  292         activation_7[0][0]               

__________________________________________________________________________________________________

bn2c_branch2b (BatchNormalizati (None, 191, 191, 4)  16          res2c_branch2b[0][0]             

__________________________________________________________________________________________________

activation_8 (Activation)       (None, 191, 191, 4)  0           bn2c_branch2b[0][0]              

__________________________________________________________________________________________________

res2c_branch2c (Conv2D)         (None, 191, 191, 8)  40          activation_8[0][0]               

__________________________________________________________________________________________________

add_3 (Add)                     (None, 191, 191, 8)  0           res2c_branch2c[0][0]             

                                                                 add_2[0][0]                      

__________________________________________________________________________________________________

res3a_branch2a (Conv2D)         (None, 96, 96, 8)    72          add_3[0][0]                      

__________________________________________________________________________________________________

bn3a_branch2a (BatchNormalizati (None, 96, 96, 8)    32          res3a_branch2a[0][0]             

__________________________________________________________________________________________________

activation_9 (Activation)       (None, 96, 96, 8)    0           bn3a_branch2a[0][0]              

__________________________________________________________________________________________________

res3a_branch2b (Conv2D)         (None, 96, 96, 8)    584         activation_9[0][0]               

__________________________________________________________________________________________________

bn3a_branch2b (BatchNormalizati (None, 96, 96, 8)    32          res3a_branch2b[0][0]             

__________________________________________________________________________________________________

activation_10 (Activation)      (None, 96, 96, 8)    0           bn3a_branch2b[0][0]              

__________________________________________________________________________________________________

res3a_branch2c (Conv2D)         (None, 96, 96, 16)   144         activation_10[0][0]              

__________________________________________________________________________________________________

res3a_branch1 (Conv2D)          (None, 96, 96, 16)   144         add_3[0][0]                      

__________________________________________________________________________________________________

bn3a_branch2c (BatchNormalizati (None, 96, 96, 16)   64          res3a_branch2c[0][0]             

__________________________________________________________________________________________________

bn3a_branch1 (BatchNormalizatio (None, 96, 96, 16)   64          res3a_branch1[0][0]              

__________________________________________________________________________________________________

add_4 (Add)                     (None, 96, 96, 16)   0           bn3a_branch2c[0][0]              

                                                                 bn3a_branch1[0][0]               

__________________________________________________________________________________________________

activation_11 (Activation)      (None, 96, 96, 16)   0           add_4[0][0]                      

__________________________________________________________________________________________________

bn3b_branch2a (BatchNormalizati (None, 96, 96, 16)   64          activation_11[0][0]              

__________________________________________________________________________________________________

activation_12 (Activation)      (None, 96, 96, 16)   0           bn3b_branch2a[0][0]              

__________________________________________________________________________________________________

res3b_branch2b (Conv2D)         (None, 96, 96, 8)    1160        activation_12[0][0]              

__________________________________________________________________________________________________

bn3b_branch2b (BatchNormalizati (None, 96, 96, 8)    32          res3b_branch2b[0][0]             

__________________________________________________________________________________________________

activation_13 (Activation)      (None, 96, 96, 8)    0           bn3b_branch2b[0][0]              

__________________________________________________________________________________________________

res3b_branch2c (Conv2D)         (None, 96, 96, 16)   144         activation_13[0][0]              

__________________________________________________________________________________________________

add_5 (Add)                     (None, 96, 96, 16)   0           res3b_branch2c[0][0]             

                                                                 activation_11[0][0]              

__________________________________________________________________________________________________

res4a_branch2a (Conv2D)         (None, 48, 48, 16)   272         add_5[0][0]                      

__________________________________________________________________________________________________

bn4a_branch2a (BatchNormalizati (None, 48, 48, 16)   64          res4a_branch2a[0][0]             

__________________________________________________________________________________________________

activation_14 (Activation)      (None, 48, 48, 16)   0           bn4a_branch2a[0][0]              

__________________________________________________________________________________________________

res4a_branch2b (Conv2D)         (None, 48, 48, 16)   2320        activation_14[0][0]              

__________________________________________________________________________________________________

bn4a_branch2b (BatchNormalizati (None, 48, 48, 16)   64          res4a_branch2b[0][0]             

__________________________________________________________________________________________________

activation_15 (Activation)      (None, 48, 48, 16)   0           bn4a_branch2b[0][0]              

__________________________________________________________________________________________________

res4a_branch2c (Conv2D)         (None, 48, 48, 64)   1088        activation_15[0][0]              

__________________________________________________________________________________________________

res4a_branch1 (Conv2D)          (None, 48, 48, 64)   1088        add_5[0][0]                      

__________________________________________________________________________________________________

bn4a_branch2c (BatchNormalizati (None, 48, 48, 64)   256         res4a_branch2c[0][0]             

__________________________________________________________________________________________________

bn4a_branch1 (BatchNormalizatio (None, 48, 48, 64)   256         res4a_branch1[0][0]              

__________________________________________________________________________________________________

add_6 (Add)                     (None, 48, 48, 64)   0           bn4a_branch2c[0][0]              

                                                                 bn4a_branch1[0][0]               

__________________________________________________________________________________________________

activation_16 (Activation)      (None, 48, 48, 64)   0           add_6[0][0]                      

__________________________________________________________________________________________________

bn4b_branch2a (BatchNormalizati (None, 48, 48, 64)   256         activation_16[0][0]              

__________________________________________________________________________________________________

activation_17 (Activation)      (None, 48, 48, 64)   0           bn4b_branch2a[0][0]              

__________________________________________________________________________________________________

res4b_branch2b (Conv2D)         (None, 48, 48, 16)   9232        activation_17[0][0]              

__________________________________________________________________________________________________

bn4b_branch2b (BatchNormalizati (None, 48, 48, 16)   64          res4b_branch2b[0][0]             

__________________________________________________________________________________________________

activation_18 (Activation)      (None, 48, 48, 16)   0           bn4b_branch2b[0][0]              

__________________________________________________________________________________________________

res4b_branch2c (Conv2D)         (None, 48, 48, 64)   1088        activation_18[0][0]              

__________________________________________________________________________________________________

add_7 (Add)                     (None, 48, 48, 64)   0           res4b_branch2c[0][0]             

                                                                 activation_16[0][0]              

__________________________________________________________________________________________________

res5a_branch2a (Conv2D)         (None, 24, 24, 32)   2080        add_7[0][0]                      

__________________________________________________________________________________________________

bn5a_branch2a (BatchNormalizati (None, 24, 24, 32)   128         res5a_branch2a[0][0]             

__________________________________________________________________________________________________

activation_19 (Activation)      (None, 24, 24, 32)   0           bn5a_branch2a[0][0]              

__________________________________________________________________________________________________

res5a_branch2b (Conv2D)         (None, 24, 24, 32)   9248        activation_19[0][0]              

__________________________________________________________________________________________________

bn5a_branch2b (BatchNormalizati (None, 24, 24, 32)   128         res5a_branch2b[0][0]             

__________________________________________________________________________________________________

activation_20 (Activation)      (None, 24, 24, 32)   0           bn5a_branch2b[0][0]              

__________________________________________________________________________________________________

res5a_branch2c (Conv2D)         (None, 24, 24, 128)  4224        activation_20[0][0]              

__________________________________________________________________________________________________

res5a_branch1 (Conv2D)          (None, 24, 24, 128)  8320        add_7[0][0]                      

__________________________________________________________________________________________________

bn5a_branch2c (BatchNormalizati (None, 24, 24, 128)  512         res5a_branch2c[0][0]             

__________________________________________________________________________________________________

bn5a_branch1 (BatchNormalizatio (None, 24, 24, 128)  512         res5a_branch1[0][0]              

__________________________________________________________________________________________________

add_8 (Add)                     (None, 24, 24, 128)  0           bn5a_branch2c[0][0]              

                                                                 bn5a_branch1[0][0]               

__________________________________________________________________________________________________

activation_21 (Activation)      (None, 24, 24, 128)  0           add_8[0][0]                      

__________________________________________________________________________________________________

res6a_branch2a (Conv2D)         (None, 12, 12, 64)   8256        activation_21[0][0]              

__________________________________________________________________________________________________

bn6a_branch2a (BatchNormalizati (None, 12, 12, 64)   256         res6a_branch2a[0][0]             

__________________________________________________________________________________________________

activation_22 (Activation)      (None, 12, 12, 64)   0           bn6a_branch2a[0][0]              

__________________________________________________________________________________________________

res6a_branch2b (Conv2D)         (None, 12, 12, 64)   36928       activation_22[0][0]              

__________________________________________________________________________________________________

bn6a_branch2b (BatchNormalizati (None, 12, 12, 64)   256         res6a_branch2b[0][0]             

__________________________________________________________________________________________________

activation_23 (Activation)      (None, 12, 12, 64)   0           bn6a_branch2b[0][0]              

__________________________________________________________________________________________________

res6a_branch2c (Conv2D)         (None, 12, 12, 512)  33280       activation_23[0][0]              

__________________________________________________________________________________________________

res6a_branch1 (Conv2D)          (None, 12, 12, 512)  66048       activation_21[0][0]              

__________________________________________________________________________________________________

bn6a_branch2c (BatchNormalizati (None, 12, 12, 512)  2048        res6a_branch2c[0][0]             

__________________________________________________________________________________________________

bn6a_branch1 (BatchNormalizatio (None, 12, 12, 512)  2048        res6a_branch1[0][0]              

__________________________________________________________________________________________________

add_9 (Add)                     (None, 12, 12, 512)  0           bn6a_branch2c[0][0]              

                                                                 bn6a_branch1[0][0]               

__________________________________________________________________________________________________

activation_24 (Activation)      (None, 12, 12, 512)  0           add_9[0][0]                      

__________________________________________________________________________________________________

avg_pool (GlobalAveragePooling2 (None, 512)          0           activation_24[0][0]              

__________________________________________________________________________________________________

dropout_1 (Dropout)             (None, 512)          0           avg_pool[0][0]                   

__________________________________________________________________________________________________

FC1 (Dense)                     (None, 1)            513         dropout_1[0][0]                  

__________________________________________________________________________________________________

activation_25 (Activation)      (None, 1)            0           FC1[0][0]                        

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

Total params: 196,557

Trainable params: 192,867

Non-trainable params: 3,690

Everything looks correct. However When I run the code, I get the following error:

Epoch 1/1

Traceback (most recent call last):

  File "C:/Users/ASista162282/Desktop/code/camleyon_17/train.py", line 114, in <module>

    training_pipeline()

  File "C:/Users/ASista162282/Desktop/code/camleyon_17/train.py", line 71, in training_pipeline

    verbose = 1)

  File "C:ProgramDataMiniconda3libsite-packageskerasenginetraining.py", line 1705, in fit

    validation_steps=validation_steps)

  File "C:ProgramDataMiniconda3libsite-packageskerasenginetraining.py", line 1188, in _fit_loop

    outs = f(ins)

  File "C:ProgramDataMiniconda3libsite-packageskerasbackendtensorflow_backend.py", line 2478, in __call__

    **self.session_kwargs)

  File "C:ProgramDataMiniconda3libsite-packagestensorflowpythonclientsession.py", line 900, in run

    run_metadata_ptr)

  File "C:ProgramDataMiniconda3libsite-packagestensorflowpythonclientsession.py", line 1111, in _run

    str(subfeed_t.get_shape())))

ValueError: Cannot feed value of shape () for Tensor 'input_1:0', which has shape '(?, 389, 389, 3)'

It doesn't make any sense. I even added the set_shape function before defining the model, and it still shows empty shape. Any help will be really appreciated. Thank you.

The way you are replacing the input layer doesn't seem to connect the new layer correctly. Try replacing this:

model_single.layers[0] = InputLayer(input_tensor=input_tensor, input_shape=(389, 389, 3))

with this:

from keras.models import Model
model_single.layers.pop(0)
new_input = InputLayer(input_tensor=input_tensor, input_shape=(389, 389, 3))
new_output = model_single(new_input)
model_single = Model(new_input, new_output)