NameError: name 'scipy' is not defined when trying to create a model-CodePudding

I'm currently trying to create a model using transfer learning, but I'm getting an error

NameError: name 'scipy' is not defined

I'm going to learn from the video. We have loaded some datasets to the computer and I am trying to convert these datasets into '.json' and '.h5' files. I had to run the code you saw in the first part and create the model. There was supposed to be a download like in the video, but instead I got an error and I can't solve it.

Here are my codes:

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense
from keras.applications.vgg16 import VGG16
import matplotlib.pyplot as plt
from glob import glob
from keras.utils import img_to_array
from keras.utils import load_img

train_path = "/Users/atakansever/Desktop/CNNN/fruits-360_dataset/fruits-360/Training/"
test_path = "/Users/atakansever/Desktop/CNNN/fruits-360_dataset/fruits-360/Test/"

# img = load_img(train_path   "Tangelo/0_100.jpg")
# plt.imshow(img)
# plt.axes("off")
# plt.show()

numberOfClass = len(glob(train_path   "/*"))
# print(numberOfClass)

vgg = VGG16()
# print(vgg.summary())

vgg_layer_list = vgg.layers
# print(vgg_layer_list)

model = Sequential()
for i in range(len(vgg_layer_list)-1):
    model.add(vgg_layer_list[i])

# print(model.summary())

for layers in model.layers:
    layers.trainable = False

model.add(Dense(numberOfClass, activation="softmax"))
# print(model.summary())

model.compile(loss = "categorical_crossentropy",optimizer = "rmsprop",metrics = ["accuracy"])

#train
train_data = ImageDataGenerator().flow_from_directory(train_path, target_size=(224,224))
test_data = ImageDataGenerator().flow_from_directory(test_path, target_size=(224,224))

batch_size = 32

hist = model.fit_generator(train_data,
steps_per_epoch=1600//batch_size,
epochs=25,
validation_data= test_data,
validation_steps=800//batch_size)

and here is the error

pyenv shell 3.9.7
atakansever@atakan-Air CNNN % pyenv shell 3.9.7
pyenv: shell integration not enabled. Run `pyenv init' for instructions.
atakansever@atakan-Air CNNN % /Users/atakansever/.pyenv/versions/3.9.7/bin/python /Users/atakansever/Desktop/CNNN/fruits.py
Metal device set to: Apple M1

systemMemory: 8.00 GB
maxCacheSize: 2.67 GB

2022-07-10 11:17:50.428036: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2022-07-10 11:17:50.428259: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
Found 67692 images belonging to 131 classes.
Found 22688 images belonging to 131 classes.
/Users/atakansever/Desktop/CNNN/fruits.py:53: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  hist = model.fit_generator(train_data, steps_per_epoch=1600//batch_size,epochs=25,validation_data= test_data,validation_steps=800//batch_size)
Traceback (most recent call last):
  File "/Users/atakansever/Desktop/CNNN/fruits.py", line 53, in <module>
    hist = model.fit_generator(train_data, steps_per_epoch=1600//batch_size,epochs=25,validation_data= test_data,validation_steps=800//batch_size)
  File "/Users/atakansever/.pyenv/versions/3.9.7/lib/python3.9/site-packages/keras/engine/training.py", line 2260, in fit_generator
    return self.fit(
  File "/Users/atakansever/.pyenv/versions/3.9.7/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/Users/atakansever/.pyenv/versions/3.9.7/lib/python3.9/site-packages/keras/preprocessing/image.py", line 2244, in apply_affine_transform
    if scipy is None:
NameError: name 'scipy' is not defined

CodePudding user response：

try pip install scipy or pip3 install scipy would solve the problem

CodePudding user response：

First, install the scipy package if it isn't already installed:

pip install scipy

and then add scipy to your imports:

import scipy # This is new!
from keras.preprocessing.image import ImageDataGenerator
# ... all your imports