I'm trying to run a CNN code from a Udemy course simply training on images of dogs and cats for classification. However I'm stuck on the training step with this value error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-14-894200829115> in <module>()
----> 1 cnn.fit(x = training_set, validation_data = test_set, epochs = 25)
~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self,
*args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y,
batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight,
sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size,
validation_freq, max_queue_size, workers, use_multiprocessing)
1061 use_multiprocessing=use_multiprocessing,
1062 model=self,
-> 1063 steps_per_execution=self._steps_per_execution)
1064
1065 # Container that configures and calls `tf.keras.Callback`s.
~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in __init__(self, x, y,
sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight,
max_queue_size, workers, use_multiprocessing, model, steps_per_execution)
1115 use_multiprocessing=use_multiprocessing,
1116 distribution_strategy=ds_context.get_strategy(),
-> 1117 model=model)
1118
1119 strategy = ds_context.get_strategy()
~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in __init__(self, x, y,
sample_weights, shuffle, workers, use_multiprocessing, max_queue_size, model, **kwargs)
914 max_queue_size=max_queue_size,
915 model=model,
--> 916 **kwargs)
917
918 @staticmethod
~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in __init__(self, x, y,
sample_weights, workers, use_multiprocessing, max_queue_size, model, **kwargs)
786 peek, x = self._peek_and_restore(x)
787 peek = self._standardize_batch(peek)
--> 788 peek = _process_tensorlike(peek)
789
790 # Need to build the Model on concrete input shapes.
~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in _
process_tensorlike(inputs)
1019 return x
1020
-> 1021 inputs = nest.map_structure(_convert_numpy_and_scipy, inputs)
1022 return nest.list_to_tuple(inputs)
1023
~\Anaconda3\lib\site-packages\tensorflow\python\util\nest.py in map_structure(func, *structure,
**kwargs)
633
634 return pack_sequence_as(
--> 635 structure[0], [func(*x) for x in entries],
636 expand_composites=expand_composites)
637
~\Anaconda3\lib\site-packages\tensorflow\python\util\nest.py in <listcomp>(.0)
633
634 return pack_sequence_as(
--> 635 structure[0], [func(*x) for x in entries],
636 expand_composites=expand_composites)
637
~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in
convert_numpy_and_scipy(x)
1014 if issubclass(x.dtype.type, np.floating):
1015 dtype = backend.floatx()
-> 1016 return ops.convert_to_tensor(x, dtype=dtype)
1017 elif scipy_sparse and scipy_sparse.issparse(x):
1018 return _scipy_sparse_to_sparse_tensor(x)
~\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in convert_to_tensor(value, dtype,
name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
1497
1498 if ret is None:
-> 1499 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1500
1501 if ret is NotImplemented:
~\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_conversion_registry.py in
_default_conversion_function(***failed resolving arguments***)
50 def _default_conversion_function(value, dtype, name, as_ref):
51 del as_ref # Unused.
---> 52 return constant_op.constant(value, dtype, name=name)
53
54
~\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py in constant(value, dtype,
shape, name)
262 """
263 return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 264 allow_broadcast=True)
265
266
~\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py in _constant_impl(value,
dtype, shape, name, verify_shape, allow_broadcast)
273 with trace.Trace("tf.constant"):
274 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
--> 275 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
276
277 g = ops.get_default_graph()
~\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py in _constant_eager_impl(ctx,
value, dtype, shape, verify_shape)
298 def _constant_eager_impl(ctx, value, dtype, shape, verify_shape):
299 """Implementation of eager constant."""
--> 300 t = convert_to_eager_tensor(value, ctx, dtype)
301 if shape is None:
302 return t
~\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py in
convert_to_eager_tensor(value, ctx, dtype)
96 dtype = dtypes.as_dtype(dtype).as_datatype_enum
97 ctx.ensure_initialized()
---> 98 return ops.EagerTensor(value, ctx.device_name, dtype)
99
100
ValueError: object __array__ method not producing an array
Below is the code snippet:
# Importing the libraries
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
tf.__version__
# Part 1 - Data Preprocessing
# Preprocessing the Training set
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
training_set = train_datagen.flow_from_directory('dataset/training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
# Preprocessing the Test set
test_datagen = ImageDataGenerator(rescale = 1./255)
test_set = test_datagen.flow_from_directory('dataset/test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
# Part 2 - Building the CNN
# Initialising the CNN
cnn = tf.keras.models.Sequential()
# Step 1 - Convolution
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=[64, 64,
3]))
# Step 2 - Pooling
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
# Adding a second convolutional layer
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
# Step 3 - Flattening
cnn.add(tf.keras.layers.Flatten())
# Step 4 - Full Connection
cnn.add(tf.keras.layers.Dense(units=128, activation='relu'))
# Step 5 - Output Layer
cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))
# Part 3 - Training the CNN
# Compiling the CNN
cnn.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Training the CNN on the Training set and evaluating it on the Test set
cnn.fit(x = training_set, validation_data = test_set, epochs = 25) # the line causing problem
As you can see, everything else works fine until the training step:
I'm using Python 3.7.0 installed from Anaconda3 on Win10. I've tried the following:
- Run on both Jupyter Notebook and local IDE (Spyder)
- Use different versions of Tensorflow, i.e. v2.3.1, v 2.3.0 and v2.2.0 (the version used in the lecture)
Neither of the above worked since the problem persists. The keras version installed is 2.4.3. However the code runs smoothly on Google Colab, where the only difference is that the data images were loaded and unzipped from DropBox with commands since Google Colab is not connected with host OS file system.
I would appreciate any insights and help on what causes the error and how to solve it.
