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

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

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

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

I clicked on the error message and it directed you to the source code.
Comment that two line and save the python script.
# if scipy is None:
# raise ImportError('Image transformations require SciPy. '
# 'Install SciPy.')
Commect code image
Then it will work perfectly.

You have to:
Install scipy pip install scipy
Restart VS code to your IDE or perhaps restart Python Kernel and rerun the code.

"AttributeError: module 'tensorflow' has no attribute 'tables_initializer' " using TFv2.2.0, Why and how to solve it?

I got errors when I want to run these lines.
import tensorflow.python.keras.backend as K
session = K.get_session()
init_op = tf.group(tf.tables_initializer(),tf.global_variables_initializer(),
tf.local_variables_initializer())
session.run(init_op)
np.random.seed(1)
tf.set_random_seed(1)
The error says: module 'tensorflow' has no attribute 'tables_initializer', and in the same form happens with global_variables_initializer, and local_variables_initializer (when I run them individually).
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-40-82a30eae4406> in <module>()
5
6 session = K.get_session()
----> 7 init_op = tf.group(tf.tables_initializer(),tf.global_variables_initializer(),
tf.local_variables_initializer())
8 session.run(init_op)
9 np.random.seed(1)
AttributeError: module 'tensorflow' has no attribute 'tables_initializer'
Please, help...

Given code is compatible with Tensorflow 1.x.
To make it work with Tensorflow 2.x, need some modification to your code, i.e.,change the library names as
tf.compat.v1.tables_initializer()
tf.compat.v1.global_variables_initializer()
tf.compat.v1.local_variables_initializer().
Find the working code snippet
import tensorflow.compat.v1 as tf
import tensorflow.python.keras.backend as K
tf.compat.v1.disable_eager_execution()
import numpy as np
session = K.get_session()
init_op = tf.group(tf.tables_initializer(),tf.global_variables_initializer(),
tf.local_variables_initializer())
session.run(init_op)
np.random.seed(1)
tf.set_random_seed(1)

Error 404 not found In postman when trying to call predict method for tensorflow-hub

I was testing a tensorflow model on Postman that uses https://tfhub.dev/google/universal-sentence-encoder-multilingual/3 from tensorflow-hub, knowing that it worked perfectly in jupyter notebook without any error, I encountered this error in postman after sending a POST request that calls predict method.
Error:
"error": "{{function_node __inference_signature_wrapper_133703}} {{function_node __inference_signature_wrapper_133703}} {{function_node __inference__wrapped_model_95698}} {{function_node __inference__wrapped_model_95698}} {{function_node __inference_restored_function_body_51031}} {{function_node __inference_restored_function_body_51031}} [_Derived_]{{function_node __inference___call___6286}} {{function_node __inference___call___6286}} Op type not registered \'SentencepieceOp\' in binary running on 329ddc874964. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) `tf.contrib.resampler` should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.\n\t [[{{node StatefulPartitionedCall}}]]\n\t [[StatefulPartitionedCall]]\n\t [[sequential/keras_layer/StatefulPartitionedCall]]\n\t [[StatefulPartitionedCall]]\n\t [[StatefulPartitionedCall]]"
with a status 404 not found.
and this is my model:
import tensorflow as tf
import numpy as np
import tensorflow_text
import pandas as pd
import random
import tensorflow_hub as hub
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import losses
from tensorflow.keras import preprocessing
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
from tensorflow.keras.layers import SpatialDropout1D
from tensorflow.keras.layers import Embedding
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
module_url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
# Import the Universal Sentence Encoder's TF Hub module
hub_layer = hub.KerasLayer(module_url, input_shape=[], dtype=tf.string, trainable=True)
#some data preprocessing
opt = keras.optimizers.Adam(learning_rate= 0.001)
model = tf.keras.Sequential()
model.add(hub_layer)
model.add(tf.keras.layers.Dense(32, activation='relu'))
model.add(tf.keras.layers.Dense(16, activation='relu'))
model.add(tf.keras.layers.Dense(1,activation='sigmoid'))
model.layers[0].trainable = False
model.compile(loss='binary_crossentropy',optimizer=opt, metrics=['accuracy'])
model.summary()
history = model.fit(np.array(tweet),np.array(sentiment),
validation_split=0.2, epochs=5, batch_size=32)
This is the request in Postman using localhost:.../arabtextclasstfhubtest:predict :
{
"signature_name": "serving_default",
"inputs":
{
"keras_layer_input":["كلامك جميل ورائع"]
}
}
I would like to know if it's a bug in tensoflow-hub or how to fix this problem.
thank you!!

It appears you are exporting a SavedModel to a server binary that answers the Postman requests. That server binary needs to link in the 'SentencepieceOp' from tensorflow_text, because your SavedModel uses it (as it should).

Freezing fine-tuned graph for TensorFlowSharp with TF 1.4

I've fine-tuned a model (using TF 1.9) from Object Detection Zoo Model and right now I am trying to freeze the graph for TensorFlowSharp using TF 1.9.
import tensorflow as tf
import os
from tensorflow.python.tools import freeze_graph
from tensorflow.core.protobuf import saver_pb2
#print("current tensorflow version: ", tf.version)
sess=tf.Session()
model_path = 'latest_cp/'
saver = tf.train.import_meta_graph('model.ckpt.meta')
saver.restore(sess,tf.train.latest_checkpoint('.')) #current dir of the checkpoint file
tf.train.write_graph(sess.graph_def, '.', 'test.pbtxt') #output in pbtxt format
freeze_graph.freeze_graph(input_graph = 'test.pbtxt',
input_binary = False,
input_checkpoint = model_path + 'model.ckpt',
output_node_names = "num_detections,detection_boxes,detection_scores,detection_classes",
output_graph = 'test.bytes' ,
clear_devices = True, initializer_nodes = "",input_saver = "",
restore_op_name = "save/restore_all", filename_tensor_name = "save/Const:0")
It worked but then after I imported it to Unity it returned the following error:
TFException: Op type not registered 'NonMaxSuppressionV3' in binary running on AK38713. Make sure the Op and Kernel are registered in the binary running in this process.
I find out that TensorFlowSharp works with TensorFlow 1.4 and when I tried to freeze graph with 1.4 it returns the same NonMaxSuppressionV3 error.
Do you know any way to solve this issue? Thank you so much for the support.

Can't use eager execution in Tensorflow 1.5 on windows7 machine

Problem
cant use eager execution in tensorflow version 1.5
code
from __future__ import absolute_import, division, print_function
import tensorflow as tf
from tensorflow.python.client import timeline
tf.enable_eager_execution()
x = tf.random_normal([0,10000])
y= tf.random_normal([10000,1000])
res = tf.matmul(x, y)
# Run the graph with full trace option
with tf.Session() as sess:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
sess.run(res, options=run_options, run_metadata=run_metadata)
# Create the Timeline object, and write it to a json
tl = timeline.Timeline(run_metadata.step_stats)
ctf = tl.generate_chrome_trace_format()
with open('timeline.json', 'w') as f:
f.write(ctf)
Stack Trace
C:\ProgramData\Anaconda3\lib\site-packages\h5py__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type.
from ._conv import register_converters as _register_converters
Traceback (most recent call last):
File "D:/Users/hello/PycharmProjects/crimeBuster/main.py", line 6, in
tf.enable_eager_execution()
AttributeError: module 'tensorflow' has no attribute 'enable_eager_execution'
Version
import tensorflow as tf
print(tf.__version__)
# 1.5.0

Back in version 1.5, eager execution was still in the contributed packages, so you need to import it explicitly; the correct usage is:
import tensorflow as tf
import tensorflow.contrib.eager as tfe
tfe.enable_eager_execution()
Also, just keep in mind that:
For eager execution, we recommend using TensorFlow version 1.8 or newer.
(from the Github page)
Version 1.7 was the first where the command tf.enable_eager_execution() was made available, i.e. eager execution was moved out of contrib (see v1.7 changes).