This is the first time I post something on this site, so I'm sorry for any mistake I could do! I'll try to give a short but effective description of the problem.
I'm using Tensorflow to train a Neural Network to classify rock-paper-scissors images. In my model I'm using an ImageDataGenerator. The problem occurs when I try to fit the model as the error "ImportError: Image transformations require SciPy. Install SciPy" appears.
I checked in my virtual environment, and the scipy package is already installed. I tried to uninstall it and install it again, but the problem is still there. I created a new virtual environment, but nothing changed.
I am using a MacBook Pro M1 16GB. Operating system is MacOS BigSur 11.6. I installed Tensorflow following the instructions in this site: https://developer.apple.com/metal/tensorflow-plugin/
Python version: 3.8.11, Tensorflow version: 2.5.0.
This is the code:
import os
import zipfile
local_zip = './rps.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('tmp/rps-train')
zip_ref.close()
local_zip = './rps-test-set.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('tmp/rps-test')
zip_ref.close()
base_dir = 'tmp/rps-train/rps'
rock_dir = os.path.join(base_dir, 'rock')
paper_dir = os.path.join(base_dir, 'paper')
scissors_dir = os.path.join(base_dir, 'scissors')
rock_files = os.listdir(rock_dir)
paper_files = os.listdir(paper_dir)
scissors_files = os.listdir(scissors_dir)
import tensorflow as tf
import keras_preprocessing
from keras_preprocessing import image
from keras_preprocessing.image import ImageDataGenerator
TRAINING_DIR = 'tmp/rps-train/rps'
training_datagen = ImageDataGenerator(rescale=1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True
)
VALIDATION_DIR = 'tmp/rps-test/rps-test-set'
validation_datagen = ImageDataGenerator(rescale=1./255)
train_generator = training_datagen.flow_from_directory(
TRAINING_DIR,
target_size=(150,150),
class_mode='categorical',
batch_size=126
)
validation_generator = validation_datagen.flow_from_directory(
VALIDATION_DIR,
target_size=(150,150),
class_mode='categorical',
batch_size=126
)
model = tf.keras.models.Sequential([
# This is the first convolution
tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(150, 150, 3)),
tf.keras.layers.MaxPooling2D(2, 2),
# The second convolution
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
# The third convolution
tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
# The fourth convolution
tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
# Flatten the results to feed into a DNN
tf.keras.layers.Flatten(),
tf.keras.layers.Dropout(0.5),
# 512 neuron hidden layer
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(3, activation='softmax')
])
model.summary()
model.compile(optimizer=tf.optimizers.RMSprop(learning_rate=0.001),
loss=tf.metrics.categorical_crossentropy,
metrics=['accuracy'])
The error occurs when I run this line of code:
model.fit_generator(train_generator,
epochs=3)
The error is:
ImportError Traceback (most recent call last)
/var/folders/j4/flhsd8lj4z7g689p_y84tfjh0000gn/T/ipykernel_89314/1416594977.py in <module>
1 import scipy
2
----> 3 model.fit_generator(train_generator,
4 epochs=3)
~/miniforge3/envs/python3.8/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1941 'will be removed in a future version. '
1942 'Please use `Model.fit`, which supports generators.')
-> 1943 return self.fit(
1944 generator,
1945 steps_per_epoch=steps_per_epoch,
~/miniforge3/envs/python3.8/lib/python3.8/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)
1131 training_utils.RespectCompiledTrainableState(self):
1132 # Creates a `tf.data.Dataset` and handles batch and epoch iteration.
-> 1133 data_handler = data_adapter.get_data_handler(
1134 x=x,
1135 y=y,
~/miniforge3/envs/python3.8/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in get_data_handler(*args, **kwargs)
1362 if getattr(kwargs["model"], "_cluster_coordinator", None):
1363 return _ClusterCoordinatorDataHandler(*args, **kwargs)
-> 1364 return DataHandler(*args, **kwargs)
1365
1366
~/miniforge3/envs/python3.8/lib/python3.8/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, distribute)
1152 adapter_cls = select_data_adapter(x, y)
1153 self._verify_data_adapter_compatibility(adapter_cls)
-> 1154 self._adapter = adapter_cls(
1155 x,
1156 y,
~/miniforge3/envs/python3.8/lib/python3.8/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)
930 self._keras_sequence = x
931 self._enqueuer = None
--> 932 super(KerasSequenceAdapter, self).__init__(
933 x,
934 shuffle=False, # Shuffle is handed in the _make_callable override.
~/miniforge3/envs/python3.8/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, workers, use_multiprocessing, max_queue_size, model, **kwargs)
807 # Since we have to know the dtype of the python generator when we build the
808 # dataset, we have to look at a batch to infer the structure.
--> 809 peek, x = self._peek_and_restore(x)
810 peek = self._standardize_batch(peek)
811 peek = _process_tensorlike(peek)
~/miniforge3/envs/python3.8/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in _peek_and_restore(x)
941 @staticmethod
942 def _peek_and_restore(x):
--> 943 return x[0], x
944
945 def _handle_multiprocessing(self, x, workers, use_multiprocessing,
~/miniforge3/envs/python3.8/lib/python3.8/site-packages/keras_preprocessing/image/iterator.py in __getitem__(self, idx)
63 index_array = self.index_array[self.batch_size * idx:
64 self.batch_size * (idx + 1)]
---> 65 return self._get_batches_of_transformed_samples(index_array)
66
67 def __len__(self):
~/miniforge3/envs/python3.8/lib/python3.8/site-packages/keras_preprocessing/image/iterator.py in _get_batches_of_transformed_samples(self, index_array)
236 if self.image_data_generator:
237 params = self.image_data_generator.get_random_transform(x.shape)
--> 238 x = self.image_data_generator.apply_transform(x, params)
239 x = self.image_data_generator.standardize(x)
240 batch_x[i] = x
~/miniforge3/envs/python3.8/lib/python3.8/site-packages/keras_preprocessing/image/image_data_generator.py in apply_transform(self, x, transform_parameters)
861 img_channel_axis = self.channel_axis - 1
862
--> 863 x = apply_affine_transform(x, transform_parameters.get('theta', 0),
864 transform_parameters.get('tx', 0),
865 transform_parameters.get('ty', 0),
~/miniforge3/envs/python3.8/lib/python3.8/site-packages/keras_preprocessing/image/affine_transformations.py in apply_affine_transform(x, theta, tx, ty, shear, zx, zy, row_axis, col_axis, channel_axis, fill_mode, cval, order)
279 """
280 if scipy is None:
--> 281 raise ImportError('Image transformations require SciPy. '
282 'Install SciPy.')
283 transform_matrix = None
ImportError: Image transformations require SciPy. Install SciPy.
