I'm training a neural network with Keras, and trying to use RandomCrop layer. I'm using a dynamic sized dataset (varying resolution), but I've found it's not currently the cause of this issue.
When I run model.fit(), after a short while, I receive the above mentioned error INVALID_ARGUMENT: required broadcastable shapes. I am able to get a summary of my model, so it's not some mismatch there.
My model works fine when I remove this layer, but I need it to reduce the size of my inputs (hence using RandomCrop).
full traceback + tensorflow status
2022-03-23 13:27:28.772937: W tensorflow/core/framework/op_kernel.cc:1733] INVALID_ARGUMENT: required broadcastable shapes
Traceback (most recent call last):
File "c:\Users\samue\Desktop\rcrop\main.py", line 37, in <module>
conv_model.fit(
File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\tensorflow\python\eager\execute.py", line 54, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node 'mean_squared_error/SquaredDifference' defined at (most recent call last):
File "C:\Program Files\Python310\lib\threading.py", line 966, in _bootstrap
self._bootstrap_inner()
File "C:\Program Files\Python310\lib\threading.py", line 1009, in _bootstrap_inner
self.run()
File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 1000, in run_step
outputs = model.train_step(data)
File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 860, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 918, in compute_loss
return self.compiled_loss(
File "C:\Users\samue\AppData\Roaming\Python\Python310\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\samue\AppData\Roaming\Python\Python310\site-packages\keras\losses.py", line 141, in __call__
losses = call_fn(y_true, y_pred)
File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\losses.py", line 245, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\losses.py", line 1329, in mean_squared_error
return backend.mean(tf.math.squared_difference(y_pred, y_true), axis=-1)
Node: 'mean_squared_error/SquaredDifference'
Detected at node 'mean_squared_error/SquaredDifference' defined at (most recent call last):
File "C:\Program Files\Python310\lib\threading.py", line 966, in _bootstrap
self._bootstrap_inner()
File "C:\Program Files\Python310\lib\threading.py", line 1009, in _bootstrap_inner
self.run()
File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 1000, in run_step
outputs = model.train_step(data)
File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 860, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 918, in compute_loss
return self.compiled_loss(
File "C:\Users\samue\AppData\Roaming\Python\Python310\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\samue\AppData\Roaming\Python\Python310\site-packages\keras\losses.py", line 141, in __call__
losses = call_fn(y_true, y_pred)
File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\losses.py", line 245, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\losses.py", line 1329, in mean_squared_error
return backend.mean(tf.math.squared_difference(y_pred, y_true), axis=-1)
Node: 'mean_squared_error/SquaredDifference'
2 root error(s) found.
(0) INVALID_ARGUMENT: required broadcastable shapes
[[{{node mean_squared_error/SquaredDifference}}]]
[[div_no_nan/ReadVariableOp/_84]]
(1) INVALID_ARGUMENT: required broadcastable shapes
[[{{node mean_squared_error/SquaredDifference}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_1308]
How to reproduce
I've create a minimal reproducible example, with only two images with resolution of [10, 10] both saved as .png with rgb colorspace.
Running main.py loads these images and tries to start training (failing with an error).
When I exclude the RandomCrop layer, it works just fine.
folder structure
/main_folder
--main.py
--/data
--001.png
--002.png
main.py
import cv2, os
import keras
import tensorflow as tf
from keras import layers
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
input_layer = keras.Input(shape=(None, None, 3))
cropped = layers.RandomCrop(32, 32)(input_layer)
out = layers.Conv2D(3, (3, 3), activation='sigmoid', padding='same')(cropped)
conv_model = keras.Model(input_layer, out)
conv_model.compile(
optimizer='adam',
loss=tf.keras.losses.MeanSquaredError()
)
conv_model.summary()
path = "data"
data = [cv2.imread(os.path.join(path, f)) / 255 for f in os.listdir(os.path.join(path))]
def data_generator():
for i in range(len(data)):
yield data[i], data[i]
dataset = tf.data.Dataset.from_generator(
data_generator,
output_types=(tf.float32, tf.float32),
output_shapes=((None, None, 3), (None, None, 3))
).batch(1)
conv_model.fit(
dataset,
epochs=1,
validation_data=dataset
)
So, I wanted to use this for an autoencoder (in the example). That means, I'd have to have the same crop done on both the input and compare image. This doesn't sound like something the RandomCrop could do, but since I'm already using a custom generator, I can implement it right there:
def data_generator():
for i in range(len(data)):
# Custom function to determine the patch size
x, x1, y, y1 = randomly_choose(data[i].shape)
yield data[i][x: x1, y: y1], data[i][x: x1, y: y1]
This gives me full power over the generation process, allowing me to include image flipping, rotating and other alterations.
Related
i am new in ML and i run into a problem when trying to make a neural network model for my project with tensorflow. It's mission is text classification for potential question that clients will ask their virtual assistant.
I took my code from this toturial: https://www.tensorflow.org/tutorials/structured_data/feature_columns
Whenever i run my code i get an error that says - cast from string to float is not supported.
After reading thourgh a lot of similar problems i havn't managed to find an answer. And i'd be happy to know if their was other parts that i could of done better.
(my csv dataset is made out of strings)
my code:
`
dataset = pd.read_csv('dataset.csv')
train, test = train_test_split(dataset, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)
batch_size = 5
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
feature_columns = []
sentence = feature_column.categorical_column_with_vocabulary_list('sentence', dataset.sentence.unique())
sentence_embedding = feature_column.embedding_column(sentence, dimension=8)
feature_columns.append(sentence_embedding)
feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
model = tf.keras.Sequential([
feature_layer,
layers.Dense(128, activation='relu'),
layers.Dropout(.1),
layers.Dense(1)
])
model.compile(optimizer='adam',loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),metrics=['accuracy'])
model.fit(train_ds,validation_data=val_ds,epochs=10)
loss, accuracy = model.evaluate(test_ds)
print("Accuracy", accuracy)
`
the error i get is:
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'sentence': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=string>}. Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'sentence': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=string>}. Consider rewriting this model with the Functional API.
2022-11-15 23:11:23.591677: W tensorflow/core/framework/op_kernel.cc:1757] OP_REQUIRES failed at cast_op.cc:121 : UNIMPLEMENTED: Cast string to float is not supported
Traceback (most recent call last):
File "C:\school & magsimim\magshimim\12th_grade_project\main.py", line 244, in <module>
main()
File "C:\school & magsimim\magshimim\12th_grade_project\main.py", line 229, in main
make_neural_network_model()
File "C:\school & magsimim\magshimim\12th_grade_project\main.py", line 141, in make_neural_network_model
model.fit(train_ds,validation_data=val_ds,epochs=10)
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\eager\execute.py", line 54, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.UnimplementedError: Graph execution error:
Detected at node 'binary_crossentropy/Cast' defined at (most recent call last):
File "C:\school & magsimim\magshimim\12th_grade_project\main.py", line 244, in <module>
main()
File "C:\school & magsimim\magshimim\12th_grade_project\main.py", line 229, in main
make_neural_network_model()
File "C:\school & magsimim\magshimim\12th_grade_project\main.py", line 141, in make_neural_network_model
model.fit(train_ds,validation_data=val_ds,epochs=10)
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\training.py", line 1564, in fit
tmp_logs = self.train_function(iterator)
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\training.py", line 1160, in train_function
return step_function(self, iterator)
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\training.py", line 1146, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\training.py", line 1135, in run_step
outputs = model.train_step(data)
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\training.py", line 994, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\training.py", line 1052, in compute_loss
return self.compiled_loss(
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\compile_utils.py", line 265, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\losses.py", line 152, in __call__
losses = call_fn(y_true, y_pred)
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\losses.py", line 272, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "C:\Users\magshimim\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\losses.py", line 2151, in binary_crossentropy
y_true = tf.cast(y_true, y_pred.dtype)
Node: 'binary_crossentropy/Cast'
Cast string to float is not supported
[[{{node binary_crossentropy/Cast}}]] [Op:__inference_train_function_951]
Process finished with exit code 1
I have a simple 2 layer Tensorflow model that I am trying to train on a dataset of equal-sized stereo audio files to tell me if the sound is coming more from the left side or the right side. This means the input is an array of 3072 by 2 arrays and the output is an array of 1's and 0's to represent left and right.
The problem is that when I run the program, it fails at model.fit() with an invalid argument error.
Code:
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 18 15:51:56 2022
#author: andre
"""
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint
from sklearn.model_selection import train_test_split
from datetime import datetime
from sklearn import metrics
from scipy.io import wavfile
import os
import glob
# Load in Right Side .WAV Data.
X1 = []
count1 = 0
database_path = "C:\\Users\\andre\\OneDrive\\Documents\\ESI2022\\MLDatabases\\Right\\"
for filename in glob.glob(os.path.join(database_path, '*.wav')):
X1.append(wavfile.read(filename)[1])
count1 = count1 + 1
# Load in Left side .WAV Data.
X2 = []
count2 = 0
database_path2 = "C:\\Users\\andre\\OneDrive\\Documents\\ESI2022\\MLDatabases\\Right\\"
for filename2 in glob.glob(os.path.join(database_path2, '*.wav')):
X2.append(wavfile.read(filename2)[1])
count2 = count2 + 1
# Get the smallest size audio file (this will be sample size input to model)
sample_size = len(X1[0])
for data in X1:
if len(data) < sample_size:
sample_size = len(data)
# Make audio data into equal size chunks
X1e = []
for i in X1:
num_chunks = len(i)//sample_size
for j in range(num_chunks):
X1e.append(i[(j+1)*sample_size-sample_size:(j+1)*sample_size])
X1 = X1e
X2e = []
for i in X2:
num_chunks = len(i)//sample_size
for j in range(num_chunks):
X2e.append(i[(j+1)*sample_size-sample_size:(j+1)*sample_size])
X2=X2e
del X1e
del X2e
# Create Output data that is the same length as the input data.
Y1 = np.ones([X1.__len__()],dtype='float32').tolist()
Y2 = np.zeros([X2.__len__()],dtype='float32').tolist()
# Concatenate Left and Right .WAV data and output data as numpy arrays.
X1.extend(X2)
X = np.asarray(X1)
Y = np.asarray(Y1+Y2).astype(np.int16)
#X=list(X)
#Y=list(Y)
# Split data into test training data.
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=0,shuffle=True)
'''
print(X[1])
time = np.linspace(0.,33792, 33792)
plt.plot(time, X[1][:,1], label="Left channel")
plt.plot(time, X[1][:,0], label="Right channel")
plt.legend()
plt.xlabel("Time [s]")
plt.ylabel("Amplitude")
plt.show()
'''
# Create the Model
model = Sequential()
# Add a LSTM layer with 1 output, and ambiguous input data length.
model.add(layers.LSTM(1,batch_input_shape=(1,sample_size,2),return_sequences=True))
model.add(layers.LSTM(1,return_sequences=False))
# Compile Model
#history = model.compile(loss='mean_absolute_error', metrics=['accuracy'],optimizer='adam',output='sparse_categorical_crossentropy')
optimizer = Adam(learning_rate=2*1e-4)
'''
history = model.compile(optimizer=optimizer, loss={
'output': 'sparse_categorical_crossentropy', },
metrics={
'output': 'sparse_categorical_accuracy', },
sample_weight_mode='temporal')
'''
history = model.compile(
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer="adam",
metrics=["accuracy"],
)
model.summary()
# Define Training Parameters
num_epochs = 200
num_batch_size = 1
# Save the most accurate model to file. (Verbosity Gives more information)
checkpointer = ModelCheckpoint(filepath="SavedModels/checkpointModel.hdf5", verbose=1,save_best_only=True)
# Start the timer
start = datetime.now()
# Train the model
model.fit(X_train,Y_train,batch_size=num_batch_size, epochs=num_epochs, validation_data=(X_test,Y_test), callbacks=[checkpointer],verbose=1)
# Get and Print Model Validation Accuracy
test_accuracy=model.evaluate(X_test,Y_test,verbose=0)
print(test_accuracy[1])
Output & error:
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_2 (LSTM) (1, 3072, 1) 16
lstm_3 (LSTM) (1, 1) 12
=================================================================
Total params: 28
Trainable params: 28
Non-trainable params: 0
_________________________________________________________________
Epoch 1/200
2022-02-07 09:40:36.348127: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2022-02-07 09:40:36.348459: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2022-02-07 09:40:43.978976: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2022-02-07 09:40:43.979029: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2022-02-07 09:40:43.985710: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-0FFTIDB
2022-02-07 09:40:43.986092: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-0FFTIDB
2022-02-07 09:40:43.990164: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-02-07 09:40:48.470415: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at sparse_xent_op.cc:103 : INVALID_ARGUMENT: Received a label value of 1 which is outside the valid range of [0, 1). Label values: 1
2022-02-07 09:58:29.070767: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at sparse_xent_op.cc:103 : INVALID_ARGUMENT: Received a label value of 1 which is outside the valid range of [0, 1). Label values: 1
Traceback (most recent call last):
File "C:\Users\andre\OneDrive\Documents\ESI2022\PythonScripts\BeltML\testML.py", line 127, in <module>
model.fit(X_train,Y_train,batch_size=num_batch_size, epochs=num_epochs, validation_data=(X_test,Y_test), callbacks=[checkpointer],verbose=1)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 58, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
InvalidArgumentError: Received a label value of 1 which is outside the valid range of [0, 1). Label values: 1
[[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits
(defined at C:\ProgramData\Anaconda3\lib\site-packages\keras\backend.py:5113)
]] [Op:__inference_train_function_9025]
Errors may have originated from an input operation.
Input Source operations connected to node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits:
In[0] sparse_categorical_crossentropy/Reshape_1 (defined at C:\ProgramData\Anaconda3\lib\site-packages\keras\backend.py:5109)
In[1] sparse_categorical_crossentropy/Reshape (defined at C:\ProgramData\Anaconda3\lib\site-packages\keras\backend.py:3561)
Operation defined at: (most recent call last)
File "C:\ProgramData\Anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\ProgramData\Anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\console\__main__.py", line 23, in <module>
start.main()
File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\console\start.py", line 328, in main
kernel.start()
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 677, in start
self.io_loop.start()
File "C:\ProgramData\Anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 199, in start
self.asyncio_loop.run_forever()
File "C:\ProgramData\Anaconda3\lib\asyncio\base_events.py", line 570, in run_forever
self._run_once()
File "C:\ProgramData\Anaconda3\lib\asyncio\base_events.py", line 1859, in _run_once
handle._run()
File "C:\ProgramData\Anaconda3\lib\asyncio\events.py", line 81, in _run
self._context.run(self._callback, *self._args)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 457, in dispatch_queue
await self.process_one()
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 446, in process_one
await dispatch(*args)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 353, in dispatch_shell
await result
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 648, in execute_request
reply_content = await reply_content
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 353, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2901, in run_cell
result = self._run_cell(
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2947, in _run_cell
return runner(coro)
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\async_helpers.py", line 68, in _pseudo_sync_runner
coro.send(None)
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3172, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3364, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3444, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "C:\Users\andre\AppData\Local\Temp/ipykernel_3604/1229251547.py", line 1, in <module>
runfile('C:/Users/andre/OneDrive/Documents/ESI2022/PythonScripts/BeltML/testML.py', wdir='C:/Users/andre/OneDrive/Documents/ESI2022/PythonScripts/BeltML')
File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 577, in runfile
exec_code(file_code, filename, ns_globals, ns_locals,
File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 465, in exec_code
exec(compiled, ns_globals, ns_locals)
File "C:\Users\andre\OneDrive\Documents\ESI2022\PythonScripts\BeltML\testML.py", line 127, in <module>
model.fit(X_train,Y_train,batch_size=num_batch_size, epochs=num_epochs, validation_data=(X_test,Y_test), callbacks=[checkpointer],verbose=1)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1216, in fit
tmp_logs = self.train_function(iterator)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 878, in train_function
return step_function(self, iterator)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 867, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 860, in run_step
outputs = model.train_step(data)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 809, in train_step
loss = self.compiled_loss(
File "C:\ProgramData\Anaconda3\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:\ProgramData\Anaconda3\lib\site-packages\keras\losses.py", line 141, in __call__
losses = call_fn(y_true, y_pred)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\losses.py", line 245, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\losses.py", line 1737, in sparse_categorical_crossentropy
return backend.sparse_categorical_crossentropy(
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\backend.py", line 5113, in sparse_categorical_crossentropy
res = tf.nn.sparse_softmax_cross_entropy_with_logits(
According to the documentation, the argument labels must be a batch_size vector with values in [0, num_classes)
From your logs:
received label value of 1 which is outside the valid range of [0, 1)
Perhaps the framework thinks that you have only one class because I also see that your Neural Network also has just 1 output.
Maybe for applying that SparseSoftmaxCrossEntropyWithLogits loss function you need 2 outputs. And your labels must be either 0 or 1.
I'm trying to use a sigmoid to join the output of two models with different embedding matrix. but I keep getting the error at the concatenate line. I have tried other suggestions from similar questions but it keeps giving the same error. I feel I'm missing something but I can't find it. please help explain. Thanks
############################ MODEL 1 ######################################
input_tensor=Input(shape=(35,))
input_layer= Embedding(vocab_size, 300, input_length=35, weights=[embedding_matrix],trainable=True)(input_tensor)
conv_blocks = []
filter_sizes = (2,3,4)
for fx in filter_sizes:
conv_layer= Conv1D(100, kernel_size=fx, activation='relu', data_format='channels_first')(input_layer) #filters=100, kernel_size=3
maxpool_layer = MaxPooling1D(pool_size=4)(conv_layer)
flat_layer= Flatten()(maxpool_layer)
conv_blocks.append(flat_layer)
conc_layer=concatenate(conv_blocks, axis=1)
graph = Model(inputs=input_tensor, outputs=conc_layer)
model = Sequential()
model.add(graph)
model.add(Dropout(0.2))
############################ MODEL 2 ######################################
input_tensor_1=Input(shape=(35,))
input_layer_1= Embedding(vocab_size, 300, input_length=35, weights=[embedding_matrix_1],trainable=True)(input_tensor_1)
conv_blocks_1 = []
filter_sizes_1 = (2,3,4)
for fx in filter_sizes_1:
conv_layer_1= Conv1D(100, kernel_size=fx, activation='relu', data_format='channels_first')(input_layer_1) #filters=100, kernel_size=3
maxpool_layer_1 = MaxPooling1D(pool_size=4)(conv_layer_1)
flat_layer_1= Flatten()(maxpool_layer_1)
conv_blocks_1.append(flat_layer_1)
conc_layer_1=concatenate(conv_blocks_1, axis=1)
graph_1 = Model(inputs=input_tensor_1, outputs=conc_layer_1)
model_1 = Sequential()
model_1.add(graph_1)
model_1.add(Dropout(0.2))
fused = concatenate([graph, graph_1], axis=-1)
prediction = Dense(3, activation='sigmoid')(fused)
model = Model(inputs=[input_tensor,input_tensor_1], outputs=[prediction])
model.compile(loss='sparse_categorical_crossentropy',optimizer='Adagrad', metrics=['accuracy'])
model.summary()
This is the error trace
Traceback (most recent call last):
File "DL_Ensemble.py", line 145, in <module>
fused = concatenate([graph, graph_1], axis= 1 )
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/keras/layers/merge.py", line 705, in concatenate
return Concatenate(axis=axis, **kwargs)(inputs)
File "/usr/pkg/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/base_layer.py", line 887, in __call__
self._maybe_build(inputs)
File "/usr/pkg/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/base_layer.py", line 2141, in _maybe_build
self.build(input_shapes)
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/keras/utils/tf_utils.py", line 306, in wrapper
output_shape = fn(instance, input_shape)
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/keras/layers/merge.py", line 378, in build
raise ValueError('A `Concatenate` layer should be called '
ValueError: A `Concatenate` layer should be called on a list of at least 2 inputs
UPDATE: I have reflected the answers given by #VivekMehta, however, I have this error.
File "DL_Ensemble.py", line 165, in <module>
model.fit([train_sequences,train_sequences], train_y, epochs=10,
verbose=False, batch_size=32, class_weight={0: 6.0, 1: 1.0, 2: 2.0})
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/keras/engine/training.py", line 709, in fit
return func.fit(
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/keras/engine/training_v2.py", line 313, in fit
training_result = run_one_epoch(
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/keras/engine/training_v2.py", line 123, in run_one_epoch
batch_outs = execution_function(iterator)
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/keras/engine/training_v2_utils.py",
line
86, in execution_function
distributed_function(input_fn))
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/eager/def_function.py", line 457, in __call__
result = self._call(*args, **kwds)
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/eager/def_function.py", line 520, in _call
return self._stateless_fn(*args, **kwds)
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/eager/function.py", line 1823, in __call__
return graph_function._filtered_call(args, kwargs) # pylint:
disable=protected-access
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/eager/function.py", line 1137, in _filtered_call
return self._call_flat(
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/eager/function.py", line 1223, in _call_flat
flat_outputs = forward_function.call(
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/eager/function.py", line 506, in call
outputs = execute.execute(
File "/usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/eager/execute.py", line 67, in quick_execute
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError:
Conv2DCustomBackpropInputOp only supports NHWC.
[[node Conv2DBackpropInput (defined at /usr/pkg/lib/python3.8/site-
packages/tensorflow_core/python/framework/ops.py:1751) ]] [Op:__inference_distributed_function_2250]
Function call stack:
distributed_function
I also wanted to add that when the code is run on a GPU as opposed to a CPU, the error occurs on the same line as before but the message changes to :
File "DL_Ensemble.py", line 166, in <module>
model.fit([train_sequences,train_sequences], train_y, epochs=10, verbose=False, batch_size=32, class_weight={0: 6.0, 1: 1.0, 2: 2.0})
File "/home/kosimadukwe/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 880, in fit
validation_steps=validation_steps)
File "/home/kosimadukwe/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 329, in model_iteration
batch_outs = f(ins_batch)
File "/home/kosimadukwe/.local/lib/python3.7/site-packages/tensorflow/python/keras/backend.py", line 3073, in __call__
self._make_callable(feed_arrays, feed_symbols, symbol_vals, session)
File "/home/kosimadukwe/.local/lib/python3.7/site-packages/tensorflow/python/keras/backend.py", line 3019, in _make_callable
callable_fn = session._make_callable_from_options(callable_opts)
File "/home/kosimadukwe/.local/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1471, in _make_callable_from_options
return BaseSession._Callable(self, callable_options)
File "/home/kosimadukwe/.local/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1425, in __init__
session._session, options_ptr, status)
File "/home/kosimadukwe/.local/lib/python3.7/site-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Conv2DCustomBackpropInputOp only supports NHWC.
[[{{node training/Adagrad/gradients/conv1d_5/conv1d/Conv2D_grad/Conv2DBackpropInput}}]]
Exception ignored in: <function BaseSession._Callable.__del__ at 0x7fe4dd06a730>
Traceback (most recent call last):
File "/home/kosimadukwe/.local/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1455, in __del__
self._session._session, self._handle, status)
File "/home/kosimadukwe/.local/lib/python3.7/site-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: No such callable handle: 94697914208640
So from you stack trace, code is throwing error at:
fused = concatenate([graph, graph_1], axis= 1 )
print(type(graph))
# output: <class 'tensorflow.python.keras.engine.training.Model'>
This error is coming because concatenate expects list of tensors to be concatenated. While you are passing graph and graph_1 which is not tensor but a Model instance.
So from your code I assume that you want to concatenate output of these two models. In that case you'll have to change above line to:
fused = concatenate([graph.outputs[0], graph_1.outputs[0]], axis=-1)
Here, graph.outputs gives list of outputs by given by Model. Since each model is giving us one output, we will take 0th index from each output.
Change this part and you'll get model summary as you are expecting.
I wanted to use the hausdorff I found here (https://github.com/N0vel/weighted-hausdorff-distance-tensorflow-keras-loss) as loss in my U-Net, but when I try to do so I get the following error:
ValueError: Dimensions must be equal, but are 3 and 2 for 'loss/conv2d_19_loss/MatMul' (op: 'MatMul') with input shapes: [?,3], [2,16384].
I don't really understand the code of the hausdorff distance, but I put the same batch size that my network uses in the loop. I also tried to print the shape of y_true and y_pred using another loss function to see the size that was wanted, but it only printed Tensor("loss/conv2d_19_loss/strided_slice:0", shape=(?, ?, ?), dtype=float32).
I tried to find the problem by following the path of the error, but I didn't uderstood the code.
Traceback (most recent call last):
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py", line 686, in _call_cpp_shape_fn_impl
input_tensors_as_shapes, status)
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimensions must be equal, but are 3 and 2 for 'loss/conv2d_19_loss/MatMul' (op: 'MatMul') with input shapes: [?,3], [2,16384].
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/etudiant_master/Documents/Marouane/Scripts/Test_parameters.py", line 367, in <module>
test_hyperparam_list('Loss', LOSS)
File "/home/etudiant_master/Documents/Marouane/Scripts/Test_parameters.py", line 339, in test_hyperparam_list
test_ligne = leave_one_out_model_test(batch_size, nb_epoch, validation_split, kernels, kernel_size, dropout_rate, pooling_size, block_number, path_test = path_test, iteration = var, metrics = metrics, optimizer = optimizer, loss = loss, activation = activation, activation2 = activation2)
File "/home/etudiant_master/Documents/Marouane/Scripts/Test_parameters.py", line 123, in leave_one_out_model_test
model = train_model.CNNs_layers(kernels = kernels, kernel_size = kernel_size, dropout_rate = dropout_rate, pooling_size = pooling_size, block_number = block_number, activation = activation, activation2 = activation2, metrics = metrics, optimizer = optimizer, loss = loss)
File "/home/etudiant_master/Documents/Marouane/Scripts/train_model.py", line 120, in CNNs_layers
model.compile(optimizer= optimizer, loss=loss, metrics=[metrics])
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/engine/training.py", line 849, in compile
output_loss = weighted_loss(y_true, y_pred, sample_weight, mask)
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/engine/training.py", line 454, in weighted
score_array = fn(y_true, y_pred)
File "/home/etudiant_master/Documents/Marouane/Scripts/Metrics.py", line 73, in Weighted_Hausdorff_loss
d_matrix = tf.sqrt(tf.maximum(tf.reshape(tf.reduce_sum(gt_b*gt_b, axis=1), (-1, 1)) + tf.reduce_sum(all_img_locations*all_img_locations, axis=1)-2*(tf.matmul(gt_b, tf.transpose(all_img_locations))), 0.0))
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py", line 2022, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 2516, in _mat_mul
name=name)
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3162, in create_op
compute_device=compute_device)
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3208, in _create_op_helper
set_shapes_for_outputs(op)
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2427, in set_shapes_for_outputs
return _set_shapes_for_outputs(op)
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2400, in _set_shapes_for_outputs
shapes = shape_func(op)
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2330, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py", line 627, in call_cpp_shape_fn
require_shape_fn)
File "/home/etudiant_master/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py", line 691, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Dimensions must be equal, but are 3 and 2 for 'loss/conv2d_19_loss/MatMul' (op: 'MatMul') with input shapes: [?,3], [2,16384].
I found nothing on hausdorff loss implementation, so I hope someone will find the problem.
Thanks a lot!
I am implementing the custom loss function and my code is as below:
def seedloss(y_true,y_pred):
count = tf.count_nonzero(y_pred)
loss = K.log(y_pred)
logval = tf.where(tf.is_inf(loss), tf.zeros_like(loss), loss)
loss = -(K.sum(logval,axis=(1,2,3))/count)
return loss
But I am getting the below error
Traceback (most recent call last):
File "trainrefinenet.py", line 69, in <module>
refinenet_model.compile(optimizer = Adam(lr=lr_init), loss = seedloss, metrics = [iou_coef])
File "/home/ssindhu/deeplab_env/lib64/python3.6/site-packages/keras/engine/training.py", line 229, in compile
self.total_loss = self._prepare_total_loss(masks)
File "/home/ssindhu/deeplab_env/lib64/python3.6/site-packages/keras/engine/training.py", line 692, in _prepare_total_loss
y_true, y_pred, sample_weight=sample_weight)
File "/home/ssindhu/deeplab_env/lib64/python3.6/site-packages/keras/losses.py", line 71, in __call__
losses = self.call(y_true, y_pred)
File "/home/ssindhu/deeplab_env/lib64/python3.6/site-packages/keras/losses.py", line 132, in call
return self.fn(y_true, y_pred, **self._fn_kwargs)
File "/home/ssindhu/metrics.py", line 32, in seedloss
loss = -(K.sum(logval,axis=(1,2,3),keepdims=True)/count)
File "/home/ssindhu/deeplab_env/lib64/python3.6/site-packages/tensorflow/python/ops/math_ops.py", line 884, in binary_op_wrapper
return func(x, y, name=name)
File "/home/ssindhu/deeplab_env/lib64/python3.6/site-packages/tensorflow/python/ops/math_ops.py", line 982, in _truediv_python3
(x_dtype, y_dtype))
TypeError: x and y must have the same dtype, got tf.float32 != tf.int64
I tried to to K.cast for int64 but still I am getting the same error. Can someone help me in understanding reason behind this error and how to resolve this
The problem is withing the count tensor as its type is tf.int64 by default according to the official documentation here.
You can solve this issue by setting the tensor type like so:
count = tf.count_nonzero(np.array([1, 2, 0]), dtype=tf.float32)