This is the code used by me to create a CNN. I have imported the images from google drive.
TRAIN_PATH = "drive/MyDrive/CXR/Dataset/Train/"
VAL_PATH = "drive/MyDrive/CXR/Dataset/Test/"
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import *
from keras.models import *
from keras.preprocessing import image
#CNN based model in Keras
model = Sequential()
model.add(Conv2D(32,kernel_size=(3,3),activation = 'relu',input_shape=(224,224,3)))
model.add(Conv2D(64,(3,3),activation = 'relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(64,(3,3),activation = 'relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(128,(3,3),activation = 'relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation = 'sigmoid'))
model.compile(loss=keras.losses.binary_crossentropy,optimizer='adam',metrics=['accuracy'])
model.summary()
#Train from scratch
train_datagen = image.ImageDataGenerator(
rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True,
)
test_dataset = image.ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'drive/MyDrive/CXR/Dataset/Train/',
target_size = (244,244),
batch_size = 32,
class_mode ='binary'
)
train_generator.class_indices
validation_generator = test_dataset.flow_from_directory(
'drive/MyDrive/CXR/Dataset/Test/',
target_size = (244,244),
batch_size = 32,
class_mode ='binary'
)
hist = model.fit(
train_generator,
steps_per_epoch=8,
epochs = 10,
validation_data = validation_generator,
validation_steps=2
)
When I call the last block of code:
hist = model.fit(
train_generator,
steps_per_epoch=8,
epochs = 10,
validation_data = validation_generator,
validation_steps=2
)
The code is working fine except the last block where I am getting an InvalidArgumentError as given below:
Epoch 1/10
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-19-f0dfa9c816cb> in <module>
4 epochs = 10,
5 validation_data = validation_generator,
----> 6 validation_steps=2
7
8 )
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
53 ctx.ensure_initialized()
54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55 inputs, attrs, num_outputs)
56 except core._NotOkStatusException as e:
57 if name is not None:
InvalidArgumentError: Graph execution error:
Detected at node 'sequential/flatten/Reshape' defined at (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance
app.start()
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 612, in start
self.io_loop.start()
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 149, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
self._run_once()
File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
handle._run()
File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.7/dist-packages/tornado/ioloop.py", line 690, in <lambda>
lambda f: self._run_callback(functools.partial(callback, future))
File "/usr/local/lib/python3.7/dist-packages/tornado/ioloop.py", line 743, in _run_callback
ret = callback()
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 787, in inner
self.run()
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 748, in run
yielded = self.gen.send(value)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 365, in process_one
yield gen.maybe_future(dispatch(*args))
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 209, in wrapper
yielded = next(result)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 268, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 209, in wrapper
yielded = next(result)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 545, in execute_request
user_expressions, allow_stdin,
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 209, in wrapper
yielded = next(result)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 306, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 536, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2855, in run_cell
raw_cell, store_history, silent, shell_futures)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2881, in _run_cell
return runner(coro)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/async_helpers.py", line 68, in _pseudo_sync_runner
coro.send(None)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3058, in run_cell_async
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3249, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3326, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-14-f0dfa9c816cb>", line 6, in <module>
validation_steps=2
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1409, in fit
tmp_logs = self.train_function(iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1051, in train_function
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1040, 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 1030, in run_step
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 889, in train_step
y_pred = self(x, training=True)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 490, in __call__
return super().__call__(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py", line 374, in call
return super(Sequential, self).call(inputs, training=training, mask=mask)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py", line 459, in call
inputs, training=training, mask=mask)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py", line 596, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/layers/reshaping/flatten.py", line 98, in call
return tf.reshape(inputs, flattened_shape)
Node: 'sequential/flatten/Reshape'
Input to reshape is a tensor with 3211264 values, but the requested shape requires a multiple of 86528
[[{{node sequential/flatten/Reshape}}]] [Op:__inference_train_function_1202]
Currently, I am using Google Colab for better performance. Can you please help me solve this problem? Why isn't my model training the data?
Related
I'm training a model to predict whether an eye in a picture is open or closed. I have this code, but I'm getting the error
Input to reshape is a tensor with 984064 values, but the requested
shape requires a multiple of 1568.
Here is my code:
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True
)
training_set = train_datagen.flow_from_directory(
'C:/Users/shtey/OneDrive/Desktop/Datasets/Eye Detection/dataset/train',
target_size=(64, 64),
batch_size=32,
class_mode='binary'
)
test_datagen = ImageDataGenerator(rescale=1./255)
test_set = test_datagen.flow_from_directory(
'C:/Users/shtey/OneDrive/Desktop/Datasets/Eye Detection/dataset/test'
)
cnn = tf.keras.models.Sequential()
cnn.add(tf.keras.layers.Conv2D(filters=8, kernel_size=3, activation='relu', input_shape=[64, 64, 3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(tf.keras.layers.Conv2D(filters=8, kernel_size=3, activation='relu'))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(tf.keras.layers.Flatten())
cnn.add(tf.keras.layers.Dense(units=8, activation='relu'))
cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))
cnn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
cnn.fit(x=training_set, validation_data=test_set, epochs = 20)
Here is the error:
InvalidArgumentError Traceback (most recent call last)
Input In [18], in <cell line: 3>()
1 cnn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
----> 3 cnn.fit(x=training_set, validation_data=test_set, epochs = 20)
File ~\anaconda3\lib\site-packages\keras\utils\traceback_utils.py:67, in filter_traceback.<locals>.error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
File ~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py:54, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
52 try:
53 ctx.ensure_initialized()
---> 54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
55 inputs, attrs, num_outputs)
56 except core._NotOkStatusException as e:
57 if name is not None:
InvalidArgumentError: Graph execution error:
Detected at node 'sequential_5/flatten_4/Reshape' defined at (most recent call last):
File "C:\Users\shtey\anaconda3\lib\runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\shtey\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\shtey\anaconda3\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "C:\Users\shtey\anaconda3\lib\site-packages\traitlets\config\application.py", line 846, in launch_instance
app.start()
File "C:\Users\shtey\anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 677, in start
self.io_loop.start()
File "C:\Users\shtey\anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 199, in start
self.asyncio_loop.run_forever()
File "C:\Users\shtey\anaconda3\lib\asyncio\base_events.py", line 601, in run_forever
self._run_once()
File "C:\Users\shtey\anaconda3\lib\asyncio\base_events.py", line 1905, in _run_once
handle._run()
File "C:\Users\shtey\anaconda3\lib\asyncio\events.py", line 80, in _run
self._context.run(self._callback, *self._args)
File "C:\Users\shtey\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 471, in dispatch_queue
await self.process_one()
File "C:\Users\shtey\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 460, in process_one
await dispatch(*args)
File "C:\Users\shtey\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 367, in dispatch_shell
await result
File "C:\Users\shtey\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 662, in execute_request
reply_content = await reply_content
File "C:\Users\shtey\anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 360, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\shtey\anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 532, in run_cell
return super().run_cell(*args, **kwargs)
File "C:\Users\shtey\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2863, in run_cell
result = self._run_cell(
File "C:\Users\shtey\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2909, in _run_cell
return runner(coro)
File "C:\Users\shtey\anaconda3\lib\site-packages\IPython\core\async_helpers.py", line 129, in _pseudo_sync_runner
coro.send(None)
File "C:\Users\shtey\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3106, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "C:\Users\shtey\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3309, in run_ast_nodes
if await self.run_code(code, result, async_=asy):
File "C:\Users\shtey\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3369, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "C:\Users\shtey\AppData\Local\Temp\ipykernel_4224\2238439442.py", line 3, in <cell line: 3>
cnn.fit(x=training_set, validation_data=test_set, epochs = 20)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\engine\training.py", line 1445, in fit
val_logs = self.evaluate(
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\engine\training.py", line 1756, in evaluate
tmp_logs = self.test_function(iterator)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\engine\training.py", line 1557, in test_function
return step_function(self, iterator)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\engine\training.py", line 1546, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\engine\training.py", line 1535, in run_step
outputs = model.test_step(data)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\engine\training.py", line 1499, in test_step
y_pred = self(x, training=False)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\engine\training.py", line 490, in __call__
return super().__call__(*args, **kwargs)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\engine\base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\engine\sequential.py", line 374, in call
return super(Sequential, self).call(inputs, training=training, mask=mask)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\engine\functional.py", line 458, in call
return self._run_internal_graph(
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\engine\functional.py", line 596, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\engine\base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "C:\Users\shtey\anaconda3\lib\site-packages\keras\layers\reshaping\flatten.py", line 98, in call
return tf.reshape(inputs, flattened_shape)
Node: 'sequential_5/flatten_4/Reshape'
Input to reshape is a tensor with 984064 values, but the requested shape requires a multiple of 1568
[[{{node sequential_5/flatten_4/Reshape}}]] [Op:__inference_test_function_6158]
I've searched around but I can't seem to find the fix.
(As mentioned by #zousan) You must provide the same target_size for the validation dataset as the training dataset which should match with the input_shape of the model definition and if you are using this test_set as validation dataset in model training.
Actually, pre-processing attributes should be the same for both the datasets to avoid causing the error.
Training dataset:
training_set = train_datagen.flow_from_directory(
'C:/Users/shtey/OneDrive/Desktop/Datasets/Eye Detection/dataset/train',
target_size=(64, 64),
batch_size=32,
class_mode='binary')
Validation dataset:
test_set = test_datagen.flow_from_directory(
'C:/Users/shtey/OneDrive/Desktop/Datasets/Eye Detection/dataset/test'
target_size=(64, 64),
batch_size=32,
class_mode='binary')
Please check this similar issue for your reference.
I have been working on converting vector data into images. The image size is a 6x6 grayscale image. And I'm making a model for classifying 6x6 grayscale images using tensorflow on google colab.
Here is an image sample(very small size image):
However, when the model.fit() function is called, the following error message occurs in epoch 1.
What is wrong?
Thanks in advance for the reply.
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import RMSprop
base_dir = '/content/drive/MyDrive/sample_img'
train_dir = os.path.join(base_dir, 'train')
test_dir = os.path.join(base_dir, 'test')
train_normal_dir = os.path.join(train_dir, 'normal')
train_attack_dir = os.path.join(train_dir, 'attack')
test_normal_dir = os.path.join(test_dir, 'normal')
test_attack_dir = os.path.join(test_dir, 'attack')
train_normal_fnames = os.listdir(train_normal_dir)
train_attack_fnames = os.listdir(train_attack_dir)
# Create Model
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(16, kernel_size=(3,3), activation='relu', input_shape=(6,6,1), padding='same'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(32, kernel_size=(3,3), activation='relu', padding='same'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dropout(0.4),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.summary()
# Compile Model
model.compile(optimizer=RMSprop(lr=0.001),
loss='binary_crossentropy',
metrics = ['accuracy'])
# Data Preprocessing
train_datagen = ImageDataGenerator( rescale = 1.0/255. )
test_datagen = ImageDataGenerator( rescale = 1.0/255. )
train_generator = train_datagen.flow_from_directory(train_dir,
batch_size=20,
class_mode='binary',
target_size=(6, 6))
validation_generator = test_datagen.flow_from_directory(test_dir,
batch_size=20,
class_mode = 'binary',
target_size = (6, 6))
# Model Fitting
history = model.fit(train_generator,
validation_data=validation_generator,
steps_per_epoch=100,
epochs=100,
validation_steps=50,
verbose=2)
Error Message
Epoch 1/100
NotFoundError Traceback (most recent call last)
<ipython-input-40-fc43f60c1de5> in <module>()
4 epochs=100,
5 validation_steps=50,
----> 6 verbose=2)
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
53 ctx.ensure_initialized()
54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55 inputs, attrs, num_outputs)
56 except core._NotOkStatusException as e:
57 if name is not None:
NotFoundError: Graph execution error:
Detected at node 'sequential_8/conv2d_16/Conv2D' defined at (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance
app.start()
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 499, in start
self.io_loop.start()
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 132, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
self._run_once()
File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
handle._run()
File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 122, in _handle_events
handler_func(fileobj, events)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 577, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 606, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 556, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-40-fc43f60c1de5>", line 6, in <module>
verbose=2)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1384, in fit
tmp_logs = self.train_function(iterator)
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 859, in train_step
y_pred = self(x, training=True)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py", line 374, in call
return super(Sequential, self).call(inputs, training=training, mask=mask)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py", line 452, in call
inputs, training=training, mask=mask)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py", line 589, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/layers/convolutional.py", line 248, in call
outputs = self.convolution_op(inputs, self.kernel)
File "/usr/local/lib/python3.7/dist-packages/keras/layers/convolutional.py", line 240, in convolution_op
name=self.__class__.__name__)
Node: 'sequential_8/conv2d_16/Conv2D'
No algorithm worked! Error messages:
[[{{node sequential_8/conv2d_16/Conv2D}}]] [Op:__inference_train_function_2706]
I am trying to learn the CNN model in deep learning and I am using a Cats-vs_Dogs dataset to begin with. I am following a video tutorial and the steps are the same, although the dataset is different and all the other solutions have vastly varied code that I am not able to understand. Can someone tell me why I am going wrong here? Thanks
import numpy as np
import pandas as pd
import tensorflow as tf
import itertools
import os
import shutil
import random
import glob
import matplotlib.pyplot as plt
import warnings
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense,Activation,Flatten,BatchNormalization,Conv2D,MaxPool2D
from tensorflow.keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix
train_batch=ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
.flow_from_directory(directory='/content/Cat-vs-Dogs/train',target_size=(244,244),classes=['cats','dogs'],batch_size=10)
valid_batch=ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
.flow_from_directory(directory='/content/Cat-vs-Dogs/valid',target_size=(244,244),classes=['cats','dogs'],batch_size=10)
test_batch=ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
.flow_from_directory(directory='/content/Cat-vs-Dogs/test',target_size=(244,244),classes=['cats','dogs'],batch_size=10,shuffle=False)
model1=Sequential([
Conv2D(filters=32,kernel_size=(3,3),activation='relu', padding='same',input_shape=(224,224,3)),
MaxPool2D(pool_size=(2,2),strides=2),
Conv2D(filters=64,kernel_size=(3,3),activation='relu', padding='same'),
MaxPool2D(pool_size=(2,2),strides=2),
Flatten(),
Dense(units=2,activation='softmax')
])
model1.summary()
model1.compile(optimizer=Adam(learning_rate=0.0001),loss='categorical_crossentropy',metrics=['accuracy'])
model1.fit(x=train_batch,validation_data=valid_batch,epochs=10,verbose=2)
```
The Error Output occurs like below
```
Epoch 1/10
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-87-3dd4821591bb> in <module>()
----> 1 model1.fit(x=train_batch,validation_data=valid_batch,epochs=10,verbose=2)
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
53 ctx.ensure_initialized()
54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55 inputs, attrs, num_outputs)
56 except core._NotOkStatusException as e:
57 if name is not None:
InvalidArgumentError: Graph execution error:
Detected at node 'sequential_4/flatten_4/Reshape' defined at (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance
app.start()
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 499, in start
self.io_loop.start()
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 132, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
self._run_once()
File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
handle._run()
File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 122, in _handle_events
handler_func(fileobj, events)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 577, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 606, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 556, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2828, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-76-3dd4821591bb>", line 1, in <module>
model1.fit(x=train_batch,validation_data=valid_batch,epochs=10,verbose=2)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1384, in fit
tmp_logs = self.train_function(iterator)
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 859, in train_step
y_pred = self(x, training=True)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py", line 374, in call
return super(Sequential, self).call(inputs, training=training, mask=mask)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py", line 452, in call
inputs, training=training, mask=mask)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py", line 589, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/layers/core/flatten.py", line 96, in call
return tf.reshape(inputs, flattened_shape)
Node: 'sequential_4/flatten_4/Reshape'
Input to reshape is a tensor with 2381440 values, but the requested shape requires a multiple of 200704
[[{{node sequential_4/flatten_4/Reshape}}]] [Op:__inference_train_function_3624]
The error is because the target size is (244, 244) and the input shape given in the model is (224, 224, 3). You can either change the target size to (224, 224) or change the input shape to (244, 244, 3).
Change the input_shape to (244, 244, 3)
Conv2D(filters=32,kernel_size=(3,3),activation='relu', padding='same',input_shape=(244, 244, 3)),
OR
Change the target_size to (224, 224) in train_batch, valid_batch and test_batch
train_batch=ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
.flow_from_directory(directory='/content/Cat-vs-Dogs/train',target_size=(224, 224),classes=['cats','dogs'],batch_size=10)
so I am new to machine learning, and I was building a cats and dogs program from a youtube tutorial when I came across this error. I have already looked through the video and confirmed that our code is the same. However, I am using google colab while she is using a jupyter notebook. Is this the issue, or do I need to change my code?
code
model = Sequential([
Conv2D(filters=32, kernel_size=(3,3,), activation = 'relu', padding = 'same', input_shape=(244, 224, 3)),
MaxPool2D(pool_size=(2,2), strides = 2),
Conv2D(filters=64, kernel_size=(3, 3), activation = 'relu', padding = 'same'),
MaxPool2D(pool_size=(2,2), strides = 2),
Flatten(),
Dense(units=2, activation='softmax'),
])
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics =['accuracy'])
model.fit(x=train_batches, validation_data=valid_batches, epochs=10, verbose=2)
error
InvalidArgumentError Traceback (most recent call last)
<ipython-input-155-2c9b6572eabb> in <module>()
----> 1 model.fit(x=train_batches, validation_data=valid_batches, epochs=1, verbose=2)
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
53 ctx.ensure_initialized()
54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55 inputs, attrs, num_outputs)
56 except core._NotOkStatusException as e:
57 if name is not None:
InvalidArgumentError: Graph execution error:
Detected at node 'sequential_16/flatten_16/Reshape' defined at (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance
app.start()
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 499, in start
self.io_loop.start()
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 132, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
self._run_once()
File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
handle._run()
File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 122, in _handle_events
handler_func(fileobj, events)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 452, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 481, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 431, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2828, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-155-2c9b6572eabb>", line 1, in <module>
model.fit(x=train_batches, validation_data=valid_batches, epochs=1, verbose=2)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1384, in fit
tmp_logs = self.train_function(iterator)
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 859, in train_step
y_pred = self(x, training=True)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py", line 374, in call
return super(Sequential, self).call(inputs, training=training, mask=mask)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py", line 452, in call
inputs, training=training, mask=mask)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py", line 589, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/layers/core/flatten.py", line 96, in call
return tf.reshape(inputs, flattened_shape)
Node: 'sequential_16/flatten_16/Reshape'
Input to reshape is a tensor with 2381440 values, but the requested shape requires a multiple of 218624
[[{{node sequential_16/flatten_16/Reshape}}]] [Op:__inference_train_function_9593]
You must change the input shape from (244, 224, 3) to (224, 224, 3)
Replace this
Conv2D(filters=32, kernel_size=(3,3,), activation = 'relu', padding = 'same', input_shape=(244, 224, 3)),
with this
Conv2D(filters=32, kernel_size=(3,3,), activation = 'relu', padding = 'same', input_shape=(224, 224, 3)),
Let us know if the issue still persists. Thanks!
I am finding it difficult to train the following model when using the 'Mobilenet_tranferLearning'. I am augmenting and loading the files from the directory using ImageDataGenerator and flow_from_directory method. What is interesting is that my code does not throw any errors when using InceptionV3, but does when I use 'Mobilenet_tranferLearning'.
I would appreciate some pointers as I believe I am using the correct loss function 'categorical_crossentropy' which I have also defined in train_generator (class_mode='categorical').
train_datagen = ImageDataGenerator(
rescale= 1./255,
shear_range= 0.2,
zoom_range= 0.2,
horizontal_flip= True,
rotation_range= 20,
width_shift_range= 0.2,
height_shift_range= 0.2,
validation_split=0.2,)
valid_datagen = ImageDataGenerator(
rescale= 1./255,
validation_split=0.2,)
train_generator = train_datagen.flow_from_directory(
'/content/fold/images/Images',
target_size= (243, 243),
color_mode= 'rgb',
batch_size= 64,
class_mode= 'categorical',
subset='training',
shuffle= True,
seed= 1337)
valid_generator = valid_datagen.flow_from_directory(
'/content/fold/images/Images',
target_size= (243, 243),
color_mode= 'rgb',
batch_size= 64,
class_mode= 'categorical',
subset='validation',
shuffle= True,
seed= 1337)
`import tensorflow_hub as hub
# from tensorflow.keras import Activations
classifier_url ="https://hub.tensorflow.google.cn/google/tf2-
preview/mobilenet_v2/feature_vector/4"
baseModel = hub.KerasLayer(classifier_url, input_shape=(224,224,3), output_shape=[1280],
name="Mobilenet")
baseModel.trainable = False # freeze mobilenet weights
myModel = Sequential(name="Mobilenet_tranferLearning")
myModel.add(baseModel)
myModel.add(Flatten())
myModel.add(Dropout(0.2))
myModel.add(Dense(120,activation='softmax'))
myModel.summary()`
myModel.compile(optimizer= 'adam', loss= 'categorical_crossentropy', metrics= ['accuracy'])
history = myModel.fit(train_generator,
epochs=25,
validation_data=valid_generator)`
I am receiving the following error:
InvalidArgumentError: Graph execution error:
Detected at node 'predict/feature_vector/SpatialSqueeze' defined at (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in
app.launch_new_instance()
File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance
app.start()
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 499, in start
self.io_loop.start()
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 132, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
self._run_once()
File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
handle._run()
File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 122, in _handle_events
handler_func(fileobj, events)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 452, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 481, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 431, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "", line 4, in
baseModel = hub.KerasLayer(classifier_url, input_shape=(224,224,3), output_shape=[1280], name="Mobilenet")
File "/usr/local/lib/python3.7/dist-packages/tensorflow_hub/keras_layer.py", line 153, in init
self._func = load_module(handle, tags, self._load_options)
File "/usr/local/lib/python3.7/dist-packages/tensorflow_hub/keras_layer.py", line 449, in load_module
return module_v2.load(handle, tags=tags, options=set_load_options)
File "/usr/local/lib/python3.7/dist-packages/tensorflow_hub/module_v2.py", line 106, in load
obj = tf.compat.v1.saved_model.load_v2(module_path, tags=tags)
Node: 'predict/feature_vector/SpatialSqueeze'
Can not squeeze dim[1], expected a dimension of 1, got 2
[[{{node predict/feature_vector/SpatialSqueeze}}]] [Op:__inference_train_function_253305]
Make sure you have the same image size (224, 224) in flow_from_directory and in the hub.KerasLayer. Here is a working example:
import tensorflow_hub as hub
import tensorflow as tf
img_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255, rotation_range=20)
flowers = tf.keras.utils.get_file(
'flower_photos',
'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
untar=True)
train_ds = img_gen.flow_from_directory(flowers, target_size=(224, 224), batch_size=32, shuffle=True)
classifier_url ="https://hub.tensorflow.google.cn/google/tf2-preview/mobilenet_v2/feature_vector/4"
baseModel = hub.KerasLayer(classifier_url, input_shape=(224,224,3), output_shape=[1280],
name="Mobilenet")
baseModel.trainable = False # freeze mobilenet weights
myModel = tf.keras.Sequential(name="Mobilenet_tranferLearning")
myModel.add(baseModel)
myModel.add(tf.keras.layers.Flatten())
myModel.add(tf.keras.layers.Dropout(0.2))
myModel.add(tf.keras.layers.Dense(5,activation='softmax'))
myModel.summary()
myModel.compile(optimizer= 'adam', loss= 'categorical_crossentropy', metrics= ['accuracy'])
history = myModel.fit(train_ds, epochs=25)