I was trying to implement a basic LSTM network using some random data, and I got the following error during execution of the code
'''
Traceback (most recent call last):
File "C:/Users/dell/Desktop/test run for LSTM thingy.py", line 39, in <module>
history = model.fit(x_train, y_train, epochs=1, batch_size=16, verbose=1)
File "C:\Users\dell\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\dell\AppData\Local\Temp\__autograph_generated_fileu1zdna1b.py", line 15, in tf__train_function
retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
ValueError: in user code:
File "C:\Users\dell\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py", line 1051, in train_function *
return step_function(self, iterator)
File "C:\Users\dell\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py", line 1040, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\dell\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py", line 1030, in run_step **
outputs = model.train_step(data)
File "C:\Users\dell\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py", line 890, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "C:\Users\dell\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py", line 948, in compute_loss
return self.compiled_loss(
File "C:\Users\dell\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\compile_utils.py", line 201, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "C:\Users\dell\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\losses.py", line 139, in __call__
losses = call_fn(y_true, y_pred)
File "C:\Users\dell\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\losses.py", line 243, in call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "C:\Users\dell\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\losses.py", line 1787, in categorical_crossentropy
return backend.categorical_crossentropy(
File "C:\Users\dell\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\backend.py", line 5119, in categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
ValueError: Shapes (None, 133, 1320) and (None, 133, 5) are incompatible
'''
This is how my code looks like at the moment:
import tensorflow as tf
x_train = tf.random.normal((28, 133, 1320))
y_train = tf.random.normal((28, 133, 1320))
model = tf.keras.Sequential()
model.add(tf.keras.layers.LSTM(5,activation='tanh',recurrent_activation='sigmoid', input_shape=(x_train.shape[1],x_train.shape[2]),return_sequences=True))
model.add(tf.keras.layers.Dense(5, activation= "softmax"))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
history = model.fit(x_train, y_train, epochs=1, batch_size=16, verbose=1)
Could anyone help me in debugging this code, as I need to use something similar in another side project which involves both X and Y input data of similar shapes, and I was not able to find a solution to the problem. I know it has something to do with the loss function, but thats all.
Shape of Y - (28, 133, 1320)
Shape of X - (28, 133, 1320)
Categories needed - 5
You are currently trying to do categorical classification with 5 classes but y has the shape (28, 133, 1320). It does not work like that. Also, when you use categorical_crossentropy, you need one-hot-encoded labels. Here is a working example as orientation:
import tensorflow as tf
x_train = tf.random.normal((28, 133, 1320))
# one-hot encoded labels
y_train = tf.keras.utils.to_categorical(tf.random.uniform((28,), maxval=5, dtype=tf.int32))
model = tf.keras.Sequential()
model.add(tf.keras.layers.LSTM(5,activation='tanh',recurrent_activation='sigmoid', input_shape=(x_train.shape[1],x_train.shape[2]), return_sequences=False))
model.add(tf.keras.layers.Dense(5, activation= "softmax"))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
history = model.fit(x_train, y_train, epochs=1, batch_size=16, verbose=1)
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Hola no se que podria hacer estoy usando los datos load_digits para hacer modelos neuronales y evaluarlos con k-fold
Este es mi codigo:
from keras.layers import BatchNormalization
def def_model():
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu',kernel_initializer='he_uniform', input_shape=(8,8,1)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(100, activation='relu')) # layer 1
model.add(Dense(10, activation='softmax')) # output
# summary
model.summary()
opt = SGD(learning_rate=0.01, momentum=0.9)
model.compile(loss='categorical_crossentropy',optimizer=opt,metrics=['accuracy'])
scores, histories = list(), list()
n_folds=5
# prepare cross validation
kfold = KFold(n_folds, shuffle=True, random_state=1)
# enumerate splits
for train_ix, test_ix in kfold.split(x_train):
# define model
model = def_model()
# select rows for train and test
x_train, y_train, x_test, y_test = x_train[train_ix], y_train[train_ix], x_train[test_ix], y_train[test_ix]
# fit model
history = model.fit(x_train, y_train, epochs=15, batch_size=128, validation_data=(x_test, y_test), verbose=0)
# evaluate model
_, acc = model.evaluate(x_test, y_test, verbose=0)
print('> %.3f' % (acc * 100.0))
# stores scores
scores.append(acc)
histories.append(history)
me da este error:
ValueError Traceback (most recent call last)
in
10 x_train, y_train, x_test, y_test = x_train[train_ix], y_train[train_ix], x_train[test_ix], y_train[test_ix]
11 # fit model
---> 12 history = model.fit(x_train, y_train, epochs=15, batch_size=128, validation_data=(x_test, y_test), verbose=0)
13 # evaluate model
14 _, acc = model.evaluate(x_test, y_test, verbose=0)
1 frames
/usr/local/lib/python3.8/dist-packages/keras/engine/training.py in tf__train_function(iterator)
13 try:
14 do_return = True
---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
16 except:
17 do_return = False
ValueError: in user code:
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1249, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1233, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1222, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1024, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1082, in compute_loss
return self.compiled_loss(
File "/usr/local/lib/python3.8/dist-packages/keras/engine/compile_utils.py", line 265, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "/usr/local/lib/python3.8/dist-packages/keras/losses.py", line 152, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.8/dist-packages/keras/losses.py", line 284, in call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.8/dist-packages/keras/losses.py", line 2004, in categorical_crossentropy
return backend.categorical_crossentropy(
File "/usr/local/lib/python3.8/dist-packages/keras/backend.py", line 5532, in categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
ValueError: Shapes (None, 10, 2) and (None, 10) are incompatible
quiero imprimir algo asi:
enter image description here
I´m training a model to predict a class of images 0 or 1, is a CNN net. When I´m trying to fit the model always get this error:
ValueError: Shapes (None,) and (None, 60, 36, 2) are incompatible
input_shape = (60, 36, 3)
# define two sets of inputs
inputA = Input(shape=input_shape)
inputB = Input(shape=input_shape)
x = Dense(8, activation="relu")(inputA)
x = Dense(4, activation="relu")(x)
x = keras.Model(inputs=inputA, outputs=x)
y = Dense(64, activation="relu")(inputB)
y = Dense(32, activation="relu")(y)
y = Dense(4, activation="relu")(y)
y = keras.Model(inputs=inputB, outputs=y)
combined = concatenate([x.output, y.output])
z = Dense(2, activation="relu")(combined)
z = Dense(2, activation="sigmoid")(z)
prediction=Dense(1, activation='sigmoid')(z)
model = keras.Model(inputs=[x.input, y.input], outputs=[prediction])
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.Adam(learning_rate=1e-3),
metrics=['accuracy'])
model.fit(
train_generator,
steps_per_epoch=25,
epochs=20,
validation_data=dev_generator,
validation_steps=5,
verbose=2
)
Error:
ValueError: in user code:
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1000, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 919, in compute_loss
y, y_pred, sample_weight, regularization_losses=self.losses)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 141, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 245, in call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1790, in categorical_crossentropy
y_true, y_pred, from_logits=from_logits, axis=axis)
File "/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 5083, in categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
ValueError: Shapes (None,) and (None, 60, 36, 2) are incompatible
How can I solve this?
I am trying to apply transfer learning (ResNetV250 & EfficientnetB0) to my binary image classification model but got a Value Error while fitting the model.
I add the final layer with the following parameter ->
layers.Dense(num_classes, activation='sigmoid', name='output_layer') where use num_classes = 1 and compile the model using loss='binary_crossentropy.
I got the following error:
ValueError: logits and labels must have the same shape, received
((None, 2) vs (None, 1)).
Any help/suggestions are welcome:
# Resnet 50 V2 feature vector
resnet_url = "https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4"
# Original: EfficientNetB0 feature vector (version 1)
efficientnet_url = "https://tfhub.dev/tensorflow/efficientnet/b0/feature-vector/1"
-----------------------------------------------------------------------------------------
def create_model(model_url, num_classes=1):
# Download the pretrained model and save it as a Keras layer
feature_extractor_layer = hub.KerasLayer(model_url,
trainable=False, # freeze the underlying patterns
name='feature_extraction_layer',
input_shape=IMAGE_SHAPE+(3,)) # define the input image shape
# Create our own model
model = tf.keras.Sequential([
feature_extractor_layer, # use the feature extraction layer as the base
layers.Dense(num_classes, activation='sigmoid', name='output_layer') # create our own output layer
])
return model
-----------------------------------------------------------------------------------------------------
# Create model
resnet_model = create_model(resnet_url, num_classes=train_data.num_classes)
# Compile
resnet_model.compile(loss='binary_crossentropy',
optimizer=tf.keras.optimizers.Adam(),
metrics=['accuracy'])
------------------------------------------------------------------------------------------------------
# Fit the model
resnet_history = resnet_model.fit(train_data,
epochs=5,
steps_per_epoch=len(train_data),
validation_data=val_data,
validation_steps=len(val_data),
# Add TensorBoard callback to model (callbacks parameter takes a list)
callbacks=[create_tensorboard_callback(dir_name="tensorflow_hub", # save experiment logs here
experiment_name="resnet50V2")]) # name of log files
And the error I got:
Saving TensorBoard log files to: tensorflow_hub/resnet50V2/20220418-221123
Epoch 1/5
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-24-196f7d30141c> in <module>()
7 # Add TensorBoard callback to model (callbacks parameter takes a list)
8 callbacks=[create_tensorboard_callback(dir_name="tensorflow_hub", # save experiment logs here
----> 9 experiment_name="resnet50V2")]) # name of log files
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
1145 except Exception as e: # pylint:disable=broad-except
1146 if hasattr(e, "ag_error_metadata"):
-> 1147 raise e.ag_error_metadata.to_exception(e)
1148 else:
1149 raise
ValueError: in user code:
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1000, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 919, in compute_loss
y, y_pred, sample_weight, regularization_losses=self.losses)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 141, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 245, in call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1932, in binary_crossentropy
backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),
File "/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 5247, in binary_crossentropy
return tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)
ValueError: `logits` and `labels` must have the same shape, received ((None, 2) vs (None, 1)).
For binary classification you don't need to use a unit in the Dense layer for each class, since that would be redundant. And in this case you can't do so in the first place, since you use the binary_crossentropy loss. Try adjusting layers.Dense(num_classes) to layers.Dense(1).
Model Set Code
EPOCHS = 1
x_tr = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
x_ts = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))
model = Sequential()
model.add(LSTM(units=390, input_shape = (1,X_train.shape[1]), return_sequences = True))
model.add(LSTM(units=260, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=190, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=160, return_sequences=True))
model.add(Dropout(0.3))
model.add(LSTM(units=60, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=90, return_sequences=True, name='output'))
model.add(Dense(6,activation='sigmoid'))
#Compiling the model
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
print(model.summary())
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
y_train = np.reshape(y_train, (y_train.shape[0], -1, y_train.shape[1]))
y_test = np.reshape(y_test, (y_test.shape[0], -1, y_test.shape[1]))
# Fitting with 1000 epochs and 20 batch size
model.fit(x_tr, y_train, validation_data=(x_ts, y_test),epochs=EPOCHS,batch_size=20)
This is the error that is showing whenever I run the Code. i don't know which type of error is existing
ValueError: in user code:
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1000, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 919, in compute_loss
y, y_pred, sample_weight, regularization_losses=self.losses)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 141, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 245, in call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1790, in categorical_crossentropy
y_true, y_pred, from_logits=from_logits, axis=axis)
File "/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 5083, in categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
ValueError: Shapes (None, 512, 2) and (None, 1, 6) are incompatible
I am trying to create an image processing CNN. I am using VGG16 to speed up some of the learning process. The creation of my CNN below works to the point of training and saving the model & weights. The issue occurs when I try to run a predict function after loading in the model.
image_gen = ImageDataGenerator()
train = image_gen.flow_from_directory('./data/train', class_mode='categorical', shuffle=False, batch_size=10, target_size=(151, 136))
val = image_gen.flow_from_directory('./data/validate', class_mode='categorical', shuffle=False, batch_size=10, target_size=(151, 136))
pretrained_model = VGG16(include_top=False, input_shape=(151, 136, 3), weights='imagenet')
pretrained_model.summary()
vgg_features_train = pretrained_model.predict(train)
vgg_features_val = pretrained_model.predict(val)
train_target = to_categorical(train.labels)
val_target = to_categorical(val.labels)
model = Sequential()
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(2, activation='softmax'))
model.compile(optimizer='rmsprop', metrics=['accuracy'], loss='categorical_crossentropy')
target_dir = './models/weights-improvement'
if not os.path.exists(target_dir):
os.mkdir(target_dir)
checkpoint = ModelCheckpoint(filepath=target_dir + 'weights-improvement-{epoch:02d}-{val_accuracy:.2f}.hdf5', monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
model.fit(vgg_features_train, train_target, epochs=100, batch_size=8, validation_data=(vgg_features_val, val_target), callbacks=callbacks_list)
model.save('./models/model')
model.save_weights('./models/weights')
I have this predict function, that I would like to load in an image, and then return the categorisation of this image that the model gives.
from keras.preprocessing.image import load_img, img_to_array
def predict(file):
x = load_img(file, target_size=(151,136,3))
x = img_to_array(x)
print(x.shape)
print(x.shape)
x = np.expand_dims(x, axis=0)
array = model.predict(x)
result = array[0]
if result[0] > result[1]:
if result[0] > 0.9:
print("Predicted answer: Buy")
answer = 'buy'
print(result)
print(array)
else:
print("Predicted answer: Not confident")
answer = 'n/a'
print(result)
else:
if result[1] > 0.9:
print("Predicted answer: Sell")
answer = 'sell'
print(result)
else:
print("Predicted answer: Not confident")
answer = 'n/a'
print(result)
return answer
The issue I am experiencing is when I run this predict function, I get the following error.
File "predict-binary.py", line 24, in predict
array = model.predict(x)
File ".venv\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1629, in predict
tmp_batch_outputs = self.predict_function(iterator)
File ".venv\lib\site-packages\tensorflow\python\eager\def_function.py", line 828, in __call__
result = self._call(*args, **kwds)
File ".venv\lib\site-packages\tensorflow\python\eager\def_function.py", line 871, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File ".venv\lib\site-packages\tensorflow\python\eager\def_function.py", line 725, in _initialize
self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
File ".venv\lib\site-packages\tensorflow\python\eager\function.py", line 2969, in _get_concrete_function_internal_garbage_collected
graph_function, _ = self._maybe_define_function(args, kwargs)
File ".venv\lib\site-packages\tensorflow\python\eager\function.py", line 3361, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File ".venv\lib\site-packages\tensorflow\python\eager\function.py", line 3196, in _create_graph_function
func_graph_module.func_graph_from_py_func(
File ".venv\lib\site-packages\tensorflow\python\framework\func_graph.py", line 990, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File ".venv\lib\site-packages\tensorflow\python\eager\def_function.py", line 634, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
File ".venv\lib\site-packages\tensorflow\python\framework\func_graph.py", line 977, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
.venv\lib\site-packages\tensorflow\python\keras\engine\training.py:1478 predict_function *
return step_function(self, iterator)
.venv\lib\site-packages\tensorflow\python\keras\engine\training.py:1468 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
.venv\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
.venv\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
.venv\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
.venv\lib\site-packages\tensorflow\python\keras\engine\training.py:1461 run_step **
outputs = model.predict_step(data)
.venv\lib\site-packages\tensorflow\python\keras\engine\training.py:1434 predict_step
return self(x, training=False)
.venv\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:1012 __call__
outputs = call_fn(inputs, *args, **kwargs)
.venv\lib\site-packages\tensorflow\python\keras\engine\sequential.py:375 call
return super(Sequential, self).call(inputs, training=training, mask=mask)
.venv\lib\site-packages\tensorflow\python\keras\engine\functional.py:424 call
return self._run_internal_graph(
.venv\lib\site-packages\tensorflow\python\keras\engine\functional.py:560 _run_internal_graph
outputs = node.layer(*args, **kwargs)
.venv\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:998 __call__
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
.venv\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:255 assert_input_compatibility
raise ValueError(
ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 8192 but received input with shape (None, 61608)
I'm assuming I need to change something between the Flatten() and Dense() layers of my model, but I'm not sure what. I attempted to add model.add(Dense(61608, activation='relu)) between these two as that seemed to be what was suggested in another post I saw (cannot find link now), but it lead to the same error. (I tried it with 8192 instead of 61608 as well). Any help is appreciated, thanks.
EDIT #1:
Changing the model creation/training code as I think it was suggested by Gerry P to this
img_shape = (151,136,3)
base_model=VGG19( include_top=False, input_shape=img_shape, pooling='max', weights='imagenet' )
x=base_model.output
x=Dense(100, activation='relu')(x)
x=Dropout(0.5)(x)
x=BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(x)
output=Dense(2, activation='softmax')(x)
model=Model(inputs=base_model.input, outputs=output)
image_gen = ImageDataGenerator()
train = image_gen.flow_from_directory('./data/train', class_mode='categorical', shuffle=False, batch_size=10, target_size=(151, 136))
val = image_gen.flow_from_directory('./data/validate', class_mode='categorical', shuffle=False, batch_size=10, target_size=(151, 136))
vgg_features_train = base_model.predict(train)
vgg_features_val = base_model.predict(val)
train_target = to_categorical(train.labels)
val_target = to_categorical(val.labels)
model.compile(optimizer='rmsprop', metrics=['accuracy'], loss='categorical_crossentropy')
model.fit(vgg_features_train, train_target, epochs=100, batch_size=8, validation_data=(vgg_features_val, val_target), callbacks=callbacks_list)
This resulted in a different input shape error of File "train-binary.py", line 37, in <module> model.fit(vgg_features_train, train_target, epochs=100, batch_size=8, validation_data=(vgg_features_val, val_target), callbacks=callbacks_list) ValueError: Input 0 is incompatible with layer model: expected shape=(None, 151, 136, 3), found shape=(None, 512)
your model is expecting to see an input for model.predict that has the same dimensions as it was trained on. In this case it is the dimensions of vgg_features_train.The input to model.predict that you are generating is for the input to the VGG model. You are essentially trying to do transfer learning so I suggest you proceed as below
base_model=tf.keras.applications.VGG19( include_top=False, input_shape=img_shape, pooling='max', weights='imagenet' )
x=base_model.output
x=Dense(100, activation='relu'))(x)
x=Dropout(0.5)(x)
x=BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(x)
output=Dense(2, activation='softmax')(x)
model=Model(inputs=base_model.input, outputs=output)
model.fit( train, epochs=100, batch_size=8, validation_data=val, callbacks=callbacks_list)
now for prediction you can use the same dimensions as you used to train the model.