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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)