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?
Related
I am a student working on a school project that needs me to identify agricultural pests from images. I am using Google colab to run the code. This is my code
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
image_size = (50, 50)
batch_size = 300
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
"/content/gdrive/My Drive/pest/train",
seed=1337,
image_size=image_size,
batch_size=batch_size,
)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
"/content/gdrive/My Drive/pest/validation",
seed=1337,
image_size=image_size,
batch_size=batch_size,
)
data_augmentation = keras.Sequential(
[
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.1),
]
)
def make_model(input_shape, num_classes):
inputs = keras.Input(shape=input_shape)
# Image augmentation block
x = data_augmentation(inputs)
# Entry block
x = layers.Rescaling(1.0 / 255)(x)
x = layers.Conv2D(32, 3, strides=2, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
x = layers.Conv2D(64, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
previous_block_activation = x # Set aside residual
for size in [128, 256, 512, 728]:
x = layers.Activation("relu")(x)
x = layers.SeparableConv2D(size, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
x = layers.SeparableConv2D(size, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.MaxPooling2D(3, strides=2, padding="same")(x)
# Project residual
residual = layers.Conv2D(size, 1, strides=2, padding="same")(
previous_block_activation
)
x = layers.add([x, residual]) # Add back residual
previous_block_activation = x # Set aside next residual
x = layers.SeparableConv2D(1024, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
x = layers.GlobalAveragePooling2D()(x)
if num_classes == 2:
activation = "sigmoid"
units = 1
else:
activation = "softmax"
units = num_classes
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(units, activation=activation)(x)
return keras.Model(inputs, outputs)
model = make_model(input_shape=image_size + (3,), num_classes=8)
keras.utils.plot_model(model, show_shapes=True)
epochs = 50
callbacks = [
keras.callbacks.ModelCheckpoint("save_at_{epoch}.h5"),
]
model.compile(
optimizer=keras.optimizers.Adam(1e-3),
loss="binary_crossentropy",
metrics=["accuracy"],
)
model.fit(
train_ds, epochs=epochs, callbacks=callbacks, validation_data=val_ds,
)
However, when I ran model.fit it gives me an error
Epoch 1/50
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-49bbab22d55e> in <module>()
10 )
11 model.fit(
---> 12 train_ds, epochs=epochs, callbacks=callbacks, validation_data=val_ds,
13 )
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, 8) vs (None, 1)).
I got the code straight from keras.io (keras documentation). Before changing num_classes=2 to num_classes=8, the code did ran but only had 0.1 accuracy. My teacher said it is a formatting error but I followed a tutorial and it ran previously. The format is training dataset in /content/gdrive/My Drive/pest/train and validation dataset in /content/gdrive/My Drive/pest/validation.
Any solutions?
The problem is that you are using binary_crossentropy loss even when your problem is not binary.
You need to use categorical_crossentropy for non binary cases.
It will also depend on your labels' shape: if they are not one-hot-encoded, you need to use sparse_categorical_crossentropy.
My advise is to use always soft max activation for the output with as much neurons as labels, since it works even for two labels:
activation = "softmax"
units = num_classes
# more code...
model.compile(
optimizer=keras.optimizers.Adam(1e-3),
loss="sparse_categorical_crossentropy",
metrics=["accuracy"],
)
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)
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 perform semantic segmentation by using the U-Net architecture.
When I fit the model:
history = model.fit(imgs_train,
masks_train,
batch_size= 5,
epochs = 5)
I keep getting the error below.
imgs_train.shape: (1500, 256, 256, 3)
masks_train.shape: (1500, 256, 256, 3)
# Building Unet using encoder and decoder blocks
from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, concatenate, Conv2DTranspose,
BatchNormalization, Dropout, Lambda
from keras.layers import Activation, MaxPool2D, Concatenate
def conv_block(input, num_filters=64):
# first conv layer
x = Conv2D(num_filters, kernel_size = (3,3), padding='same')(input)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# second conv layer
x = Conv2D(num_filters, kernel_size= (3,3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
return x
def encoder_block(input, num_filters=64):
# conv block
x = conv_block(input,num_filters)
# maxpooling
p = MaxPool2D(strides = (2,2))(x)
p = Dropout(0.4)(p)
return x,p
def decoder_block(input, skip_features, num_filters=64):
x = Conv2DTranspose(num_filters, (2,2), strides=2, padding='same')(input)
x = Concatenate()([x, skip_features])
x = conv_block(x, num_filters)
return x
num_classes=7
def unet_architect(input_shape=(256,256,3)):
""" Input Layer """
inputs = Input(input_shape)
""" Encoder """
s1,p1 = encoder_block(inputs, 64)
s2,p2 = encoder_block(p1,128)
s3,p3 = encoder_block(p2, 256)
s4,p4 = encoder_block(p3, 512)
""" Bridge """
b1 = conv_block(p4,1024)
""" Decoder """
d1 = decoder_block(b1, s4, 512)
d2 = decoder_block(d1, s3, 256)
d3 = decoder_block(d2, s2, 128)
d4 = decoder_block(d3, s1, 64)
""" Output Layer """
outputs = Conv2D(num_classes, (1,1), padding='same', activation = 'softmax')(d4)
model = Model(inputs, outputs, name='U-Net')
return model
model = unet_architect()
model.compile(optimizer = 'adam' ,
loss = 'sparse_categorical_crossentropy',
metrics=['accuracy'])
I tried to change sparse_categorical_crossentropy to categorical_cross_entropy, another error shows up.
And when I change the batch size while fitting the model, the logits.shape and labels.shape changes accordingly.
ERROR
Epoch 1/5
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-35-4c0704d8f65d> in <module>()
2 masks_train,
3 batch_size= 10,
----> 4 epochs = 5)
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 1863, in sparse_categorical_crossentropy
y_true, y_pred, from_logits=from_logits, axis=axis)
File "/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 5203, in sparse_categorical_crossentropy
labels=target, logits=output)
ValueError: `labels.shape` must equal `logits.shape` except for the last dimension. Received: labels.shape=(1966080,) and logits.shape=(655360, 7)
NOTEBOOK LINK:https://github.com/Tamimi123600/Deep-Learning/blob/main/Image_Segmentation1.ipynb
Thanks in advance
Why are your mask image (GT targets) of shape 1500, 256, 256, 3, and not 1500, 256, 256? You have num_classes=7 so your GT images should have a single channel with values {0...6} representing the class of each pixel.
Please check how you load and process your target images -- the issue is there.
I am using the below code where I am trying to train a 1d CNN, my x_train data shape is (10027, 5, 14) and y_train shape is (10027,4). But I am getting an error (below the code)
regarding the shape compatibility.
model = keras.models.Sequential([
keras.layers.Conv1D(filters=20, kernel_size=4, strides=2, padding="valid",
input_shape=(n_timesteps,n_features))
])
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
verbose, epochs, batch_size = 0, 10, 5
model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=verbose)```
Below error
```ValueError: in user code:
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:755 train_step
loss = self.compiled_loss(
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/compile_utils.py:203 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/losses.py:152 __call__
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/losses.py:256 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/losses.py:1537 categorical_crossentropy
return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/backend.py:4833 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/tensor_shape.py:1134 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, 4) and (None, 1, 20) are incompatible```
I mean, the last layer of your network is a conv1D with filters=20, so the output of your network will be (batch, 1, 20), and you give a Y with a shape of (batch, 4).
The loss don't understand what it has to compare, since the two arrays are not matching at all.
If you want to have something working you should give a last layer compatible with your Y :
model = keras.models.Sequential([
keras.layers.Conv1D(filters=20, kernel_size=4, strides=2, padding="valid",
input_shape=(n_timesteps,n_features)),
keras.layers.Flatten(),
keras.layers.Dense(4, activation = 'softmax')
])