import tensorflow as tf
import pandas as pd
from google.colab import drive
drive.mount('/content/drive')
import os
import numpy as np
import cv2
import matplotlib.pyplot as plt
from random import randint
from sklearn.model_selection import train_test_split
import keras
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.image import resize
image_path=("/content/drive/MyDrive/Colab Notebooks/data/Images/")
label_path= pd.read_csv("/content/drive/MyDrive/Colab Notebooks/data/metadata.csv")
label_data=pd.DataFrame(label_path)
label_data
class_names = ('clean', 'dirty')
num_classes = len(class_names)
img_size = (128, 128, 3)
print(f'{num_classes} classes: {class_names}\nimage size: {img_size}')
labels = []
images = []
for image in label_data.iloc:
images.append(np.asarray(cv2.resize(cv2.imread(image_path + image[0], cv2.IMREAD_COLOR), img_size[0:2])[:, :, ::-1]))
# labels will be in the form of a vector: [0, 1] or [1, 0]
label = np.zeros(num_classes)
label[image[1]] = 1
labels.append(label)
labels = np.asarray(labels)
images = np.asarray(images)
print(f'\nlabels shape: {labels.shape}')
print(f'images shape: {images.shape}')
#train test split
X_train, X_test, Y_train, Y_test=train_test_split(images, labels, test_size=0.3, random_state=42)
print(X_train.shape, "\n", Y_train.shape, "\n", X_test.shape, "\n" , Y_test.shape)
#Output: (165, 128, 128, 3)
(165, 2)
(72, 128, 128, 3)
(72, 2)
train_image_generator=tf.keras.preprocessing.image.ImageDataGenerator(shear_range=0.5, zoom_range=0.2, vertical_flip=True, horizontal_flip=True)
train_image_generator= train_image_generator.flow(X_train, Y_train)
test_image_generator=tf.keras.preprocessing.image.ImageDataGenerator(shear_range=0.5, zoom_range=0.2, vertical_flip=True, horizontal_flip=True)
test_image_generator= test_image_generator.flow(X_test, Y_test)
base_model=tf.keras.applications.MobileNetV2(weights="imagenet", include_top=False, input_shape=(224, 224, 3))
output=base_model.output
output = tf.keras.layers.GlobalAveragePooling2D()(output)
output = tf.keras.layers.Dense(1024, activation="relu")(output)
output = tf.keras.layers.Dense(512, activation="relu")(output)
output = tf.keras.layers.Dense(3, activation="softmax")(output)
model = tf.keras.Model(inputs=base_model.input, outputs=output)
print(X_train.shape, "\n", Y_train.shape, "\n", X_test.shape, "\n" , Y_test.shape)
model.compile(
optimizer='adam',
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True, label_smoothing=0.1),
metrics=['accuracy'])
checkpoint_callback = ModelCheckpoint('cnn_model/model{epoch:02d}')
train_size = X_train.shape[0]
valid_size = X_test.shape[0]
steps_per_epoch = train_size // 200
validation_steps = valid_size // 200
hist = model.fit(train_image_generator, batch_size=200, epochs=20, verbose=1, callbacks=[checkpoint_callback],
validation_data=test_image_generator,shuffle = True)
#output:
(165, 128, 128, 3)
(165, 2)
(72, 128, 128, 3)
(72, 2)
Epoch 1/20
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-62-9476c3b0457e> in <cell line: 14>()
12 validation_steps = valid_size // 200
13
---> 14 hist = model.fit(train_image_generator, batch_size=200, epochs=20, verbose=1, callbacks=[checkpoint_callback],
15 validation_data=test_image_generator,shuffle = True)
1 frames
/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
51 try:
52 ctx.ensure_initialized()
---> 53 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
54 inputs, attrs, num_outputs)
55 except core._NotOkStatusException as e:
InvalidArgumentError: Graph execution error:
Detected at node 'categorical_crossentropy/softmax_cross_entropy_with_logits' defined at (most recent call last):
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.10/dist-packages/colab_kernel_launcher.py", line 37, in <module>
ColabKernelApp.launch_instance()
File "/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py", line 992, in launch_instance
app.start()
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py", line 619, in start
self.io_loop.start()
File "/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py", line 195, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.10/asyncio/base_events.py", line 603, in run_forever
self._run_once()
File "/usr/lib/python3.10/asyncio/base_events.py", line 1909, in _run_once
handle._run()
File "/usr/lib/python3.10/asyncio/events.py", line 80, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py", line 685, in <lambda>
lambda f: self._run_callback(functools.partial(callback, future))
File "/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py", line 738, in _run_callback
ret = callback()
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 825, in inner
self.ctx_run(self.run)
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 786, in run
yielded = self.gen.send(value)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 361, in process_one
yield gen.maybe_future(dispatch(*args))
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 261, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 539, in execute_request
self.do_execute(
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py", line 302, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py", line 539, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 2975, in run_cell
result = self._run_cell(
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3030, in _run_cell
return runner(coro)
File "/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py", line 78, in _pseudo_sync_runner
coro.send(None)
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3257, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3473, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3553, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-62-9476c3b0457e>", line 14, in <cell line: 14>
hist = model.fit(train_image_generator, batch_size=200, epochs=20, verbose=1, callbacks=[checkpoint_callback],
File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1742, in fit
tmp_logs = self.train_function(iterator)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1338, in train_function
return step_function(self, iterator)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1322, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1303, in run_step
outputs = model.train_step(data)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1081, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1139, in compute_loss
return self.compiled_loss(
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/compile_utils.py", line 265, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "/usr/local/lib/python3.10/dist-packages/keras/src/losses.py", line 142, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.10/dist-packages/keras/src/losses.py", line 268, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.10/dist-packages/keras/src/losses.py", line 2122, in categorical_crossentropy
return backend.categorical_crossentropy(
File "/usr/local/lib/python3.10/dist-packages/keras/src/backend.py", line 5566, in categorical_crossentropy
return tf.nn.softmax_cross_entropy_with_logits(
Node: 'categorical_crossentropy/softmax_cross_entropy_with_logits'
logits and labels must be broadcastable: logits_size=[32,3] labels_size=[32,2]
[[{{node categorical_crossentropy/softmax_cross_entropy_with_logits}}]] [Op:__inference_train_function_60472]
How can I solve this???
Welcome to the TensorFlow Forum!
There are 3 mistakes observed in the above mentioned code.
- You have 2 labels in the dataset but you have mentioned in the final Dense layers as 3.
output = tf.keras.layers.Dense(3, activation="softmax")(output) - The input_shape
(224, 224, 3)in base_model is not matching with the dataset input_shape(128, 128, 3) - You have already defined the
Softmaxactivation function in the final layer of the model which will collect the appropriate logits while model training, but you again defined thefrom_logits=Truein theCategoricalCrossentropyloss function of model.compile().
Please check the correct model definition and model compile code as below and try running the code again:
base_model=tf.keras.applications.MobileNetV2(weights="imagenet", include_top=False, input_shape=(128, 128, 3))
output=base_model.output
output = tf.keras.layers.GlobalAveragePooling2D()(output)
output = tf.keras.layers.Dense(1024, activation="relu")(output)
output = tf.keras.layers.Dense(512, activation="relu")(output)
output = tf.keras.layers.Dense(2, activation="softmax")(output)
model = tf.keras.Model(inputs=base_model.input, outputs=output)
model.compile(optimizer='adam',
loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1),
metrics=['accuracy'])
(Attaching the replicated gist for your reference.)