CODEWITHSUNDEEP
pythontensorflowmachine-learningkerasdeep-learning
Ahmed Alshoki
Ahmed Alshoki

Reputation: 31

ValueError: Attempt to convert a value (None) with an unsupported type (<class 'NoneType'>) to a Tensor. Flatten Layer

I am trying using VGG16 from Keras , I marked marked include_top=false
But I faced error that say ValueError: Attempt to convert a value (None) with an unsupported type (<class 'NoneType'>) to a Tensor.
Here is the code : 

input_shape = (150,150,3)
model_1 = VGG16(weights='imagenet',include_top=False,input_shape=input_shape)
Last_layer=model_1.layers[-1].output
print(Last_layer)
print(type(Last_layer))
Model_Vgg=keras.layers.Flatten()(Last_layer) #<---- error rised here
 
#Model_Vgg=keras.Model(model.input,layer_output)

Model_Vgg = layers.Dropout(0.5)(Model_Vgg)


Model_Vgg = layers.Dense(units=3, activation='softmax') (Model_Vgg)

model = keras.Model(inputs =model_1.input,outputs = Model_Vgg )
model.compile(loss='categorical_crossentropy',optimizer=optimizers.SGD(lr=0.005708),metrics=['accuracy'])

monitor = EarlyStopping(monitor='accuracy',patience=50, mode='auto', restore_best_weights=True)
model.fit(X_Train,Y_Train,callbacks=[monitor],epochs=280,verbose=0)
(loss, accuracy) = model.evaluate(X_Test, Y_Test, batch_size=32, verbose=50)
print("[INFO] loss={:.4f}, accuracy: {:.4f}%".format(loss,accuracy * 100))

And it show that print(type(Last_layer)) = <class 'keras.engine.keras_tensor.KerasTensor'>
I have no idea why the line refers to None type object

Upvotes: 2

Views: 10584

Answers (3)

Ahmed Alshoki
Ahmed Alshoki

Reputation: 31

I found this solution and It worked for me

def Create_Model():
  #input_shape = (150,150,3)
  model_1 = VGG16(weights='imagenet',include_top=False)
  input = keras.layers.Input(shape=(150,150,3))
  Last_layer=model_1(input)

  Model_Vgg=keras.layers.Flatten()(Last_layer)   
  #Model_Vgg=keras.Model(model.input,layer_output)
  Model_Vgg = layers.Dropout(0.5)(Model_Vgg)
  Model_Vgg = layers.Dense(units=3, activation='softmax') (Model_Vgg)
  model = keras.Model(inputs =input,outputs = Model_Vgg )
  return model

Upvotes: 0

zakk616
zakk616

Reputation: 1552

i had the same issue with code:

from keras.layers import Dense, Flatten
x = vgg.output(Flatten())

then i changed it to

from tensorflow.keras import layers
x = layers.Flatten()(vgg.output)

and it worked.

Upvotes: 1

user11530462
user11530462

Reputation:

I was able to replicate your issue as shown below

import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.applications.vgg16 import VGG16

input_shape = (150,150,3)
model_1 = VGG16(weights='imagenet',include_top=False,input_shape=input_shape)
Last_layer=model_1.layers[-1].output
#print(Last_layer)
#print(type(Last_layer))
Model_Vgg=keras.layers.Flatten()(Last_layer)
Model_Vgg = layers.Dropout(0.5)(Model_Vgg)
Model_Vgg = layers.Dense(units=3, activation='softmax') (Model_Vgg)

model = keras.Model(inputs =model_1.input,outputs = Model_Vgg )
model.compile(loss='categorical_crossentropy',optimizer=tf.keras.optimizers.SGD(learning_ratek=0.005708),metrics=['accuracy'])

Output:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-20-3d087167b224> in <module>()
      8 #print(Last_layer)
      9 #print(type(Last_layer))
---> 10 Model_Vgg=keras.layers.Flatten()(Last_layer)
     11 Model_Vgg = layers.Dropout(0.5)(Model_Vgg)
     12 Model_Vgg = layers.Dense(units=3, activation='softmax') (Model_Vgg)

5 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
     96       dtype = dtypes.as_dtype(dtype).as_datatype_enum
     97   ctx.ensure_initialized()
---> 98   return ops.EagerTensor(value, ctx.device_name, dtype)
     99 
    100 

ValueError: Attempt to convert a value (None) with an unsupported type (<class 'NoneType'>) to a Tensor.

Fixed code:

Your issue can be resolved once you replace keras.layers.Flatten() to layers.Flatten().

Working code as shown below

import tensorflow as tf
from tensorflow.keras import layers, Model
from tensorflow.keras.applications.vgg16 import VGG16

input_shape = (150,150,3)
model_1 = VGG16(weights='imagenet',include_top=False,input_shape=input_shape)
Last_layer=model_1.layers[-1].output
print(Last_layer)
print(type(Last_layer))
Model_Vgg=layers.Flatten()(Last_layer)
Model_Vgg = layers.Dropout(0.5)(Model_Vgg)
Model_Vgg = layers.Dense(units=3, activation='softmax') (Model_Vgg)

model = Model(inputs =model_1.input,outputs = Model_Vgg )
model.compile(loss='categorical_crossentropy',optimizer=tf.keras.optimizers.SGD(learning_rate=0.005708),metrics=['accuracy'])

Output:

KerasTensor(type_spec=TensorSpec(shape=(None, 4, 4, 512), dtype=tf.float32, name=None), name='block5_pool/MaxPool:0', description="created by layer 'block5_pool'")
<class 'tensorflow.python.keras.engine.keras_tensor.KerasTensor'>

Note: You should never mix keras and tensorflow.

Upvotes: 1

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