Develop Reference

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.

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).

OSError: SavedModel file does not exist at: ../dnn/mpg_model.h5/{saved_model.pbtxt|saved_model.pb}

**
code editor: vscode
cmd: anaconda prompt
I followed the tutorial but why this error?
**
first error was ModuleNotFoundError: No module named 'tensorflow'
but i make env and install it
second error was ModuleNotFoundError: No module named 'flask'
but i make env and install it
i fix them and they work on python
How can I solve this?
# T81-558: Applications of Deep Neural Networks
# Module 13: Advanced/Other Topics
# Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)
# For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/).
# Deploy simple Keras tabular model with Flask only.
from flask import Flask, request, jsonify
import uuid
import os
from tensorflow.keras.models import load_model
import numpy as np
app = Flask(__name__)
# Used for validation
EXPECTED = {
"cylinders":{"min":3,"max":8},
"displacement":{"min":68.0,"max":455.0},
"horsepower":{"min":46.0,"max":230.0},
"weight":{"min":1613,"max":5140},
"acceleration":{"min":8.0,"max":24.8},
"year":{"min":70,"max":82},
"origin":{"min":1,"max":3}
}
# Load neural network when Flask boots up
model = load_model(os.path.join("../dnn/","mpg_model.h5"))
#app.route('/api/mpg', methods=['POST'])
def calc_mpg():
content = request.json
errors = []
# Check for valid input fields
for name in content:
if name in EXPECTED:
expected_min = EXPECTED[name]['min']
expected_max = EXPECTED[name]['max']
value = content[name]
if value < expected_min or value > expected_max:
errors.append(f"Out of bounds: {name}, has value of: {value}, but should be between {expected_min} and {expected_max}.")
else:
errors.append(f"Unexpected field: {name}.")
# Check for missing input fields
for name in EXPECTED:
if name not in content:
errors.append(f"Missing value: {name}.")
if len(errors) <1:
# Predict
x = np.zeros( (1,7) )
x[0,0] = content['cylinders']
x[0,1] = content['displacement']
x[0,2] = content['horsepower']
x[0,3] = content['weight']
x[0,4] = content['acceleration']
x[0,5] = content['year']
x[0,6] = content['origin']
pred = model.predict(x)
mpg = float(pred[0])
response = {"id":str(uuid.uuid4()),"mpg":mpg,"errors":errors}
else:
# Return errors
response = {"id":str(uuid.uuid4()),"errors":errors}
print(content['displacement'])
return jsonify(response)
if __name__ == '__main__':
app.run(host= '0.0.0.0',debug=True)
#conda
(tf-gpu) (HelloWold) C:\Users\ASUS\t81_558_deep_learning\py>python mpg_server_1.py
2020-05-09 17:25:38.498181: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
Traceback (most recent call last):
File "mpg_server_1.py", line 26, in <module>
model = load_model(os.path.join("../dnn/","mpg_model.h5"))
File "C:\Users\ASUS\Envs\HelloWold\lib\site-packages\tensorflow\python\keras\saving\save.py", line 189, in load_model
loader_impl.parse_saved_model(filepath)
File "C:\Users\ASUS\Envs\HelloWold\lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 113, in parse_saved_model
constants.SAVED_MODEL_FILENAME_PB))
OSError: SavedModel file does not exist at: ../dnn/mpg_model.h5/{saved_model.pbtxt|saved_model.pb}
from
https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_13_01_flask.ipynb
https://www.youtube.com/watch?v=H73m9XvKHug&t=1056s

The error occurs because your code is trying to load a model that does not exist. From the Notebook file you linked, you will most likely have to run the following:
from werkzeug.wrappers import Request, Response
from flask import Flask
app = Flask(__name__)
#app.route("/")
def hello():
return "Hello World!"
if __name__ == '__main__':
from werkzeug.serving import run_simple
run_simple('localhost', 9000, app)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping
import pandas as pd
import io
import os
import requests
import numpy as np
from sklearn import metrics
df = pd.read_csv(
"https://data.heatonresearch.com/data/t81-558/auto-mpg.csv",
na_values=['NA', '?'])
cars = df['name']
# Handle missing value
df['horsepower'] = df['horsepower'].fillna(df['horsepower'].median())
# Pandas to Numpy
x = df[['cylinders', 'displacement', 'horsepower', 'weight',
'acceleration', 'year', 'origin']].values
y = df['mpg'].values # regression
# Split into validation and training sets
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.25, random_state=42)
# Build the neural network
model = Sequential()
model.add(Dense(25, input_dim=x.shape[1], activation='relu')) # Hidden 1
model.add(Dense(10, activation='relu')) # Hidden 2
model.add(Dense(1)) # Output
model.compile(loss='mean_squared_error', optimizer='adam')
monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, verbose=1, mode='auto',
restore_best_weights=True)
model.fit(x_train,y_train,validation_data=(x_test,y_test),callbacks=[monitor],verbose=2,epochs=1000)
pred = model.predict(x_test)
# Measure RMSE error. RMSE is common for regression.
score = np.sqrt(metrics.mean_squared_error(pred,y_test))
print(f"After load score (RMSE): {score}")
model.save(os.path.join("./dnn/","mpg_model.h5"))
This will train and save the model that your code is loading.
It also looks like you have a small typo on the line: model = load_model(os.path.join("../dnn/","mpg_model.h5")) which should be changed to model = load_model(os.path.join("./dnn/","mpg_model.h5"))

I was getting the same error trying to load a .h5 model on a raspberry pi.
OSError: SavedModel file does not exist at: ... {saved_model.pbtxt|saved_model.pb}
sudo apt install python3-h5py
Seemed to have solved the issue.
reference

If on windows, the path to the model can cause the error.
For a sanity check, try placing the model in the same folder as the file that you are calling. Then fix your path to call the model from the same folder. This fixed my error.
If this works, then you can figure out how to fix the path issue (perhaps try providing an absolute path).

I got the same error, and I solved it by running the model again and save it with .pb extension instead with the .h5 or .hdf5 extension
Then use the tf.keras.models.load_model('D:\\model_name.pb') using double backslash
I had that error in windows and could solve it

How to use Handwriting Recognition model as an API?

I have created one machine learning model using Tensorflow and Keras by using IAM dataset. How to load this model as an API to predict an image? When I was trying to integrate It shows error
return self.function(inputs, **arguments)
File "test2.py", line 136, in resize_image
return tf.image.resize_images(image,[56,56])
NameError: name 'tf' is not defined
I have load model using from keras.models import load_model and trying to predict image handwriting. low_loss.hdf5 is model which I try to integrate.
def testmodel(image_path):
global model
# load the pre-trained Keras model
model = load_model('low_loss.hdf5')
model.summary()
img = Image.open(image_path).convert("L")
img = np.resize(image_path, (28,28,1))
im2arr = np.array(img)
im2arr = im2arr.reshape(1,28,28,1)
y_pred = model.predict_classes(im2arr)
return y_pred
I wish to predict image Handwritten data.

your error is about tf which is not loaded.
try:
import tensorflow as tf

You were getting an error because you have not imported TensorFlow in your code or if you have imported you have not given an alias.
import tensorflow as tf

AttributeError: 'Sequential' object has no attribute 'output_names'

I have got some problem for the below code
of the following line
new_model = load_model('124446.model', custom_objects=None, compile=True)
Here is the code:
import tensorflow as tf
from tensorflow.keras.models import load_model
mnist = tf.keras.datasets.mnist
(x_train,y_train), (x_test,y_test) = mnist.load_data()
x_train = tf.keras.utils.normalize(x_train,axis=1)
x_test = tf.keras.utils.normalize(x_test,axis=1)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10,activation=tf.nn.softmax))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train,y_train,epochs=3)
tf.keras.models.save_model(model,'124446.model')
val_loss, val_acc = model.evaluate(x_test,y_test)
print(val_loss, val_acc)
new_model = load_model('124446.model', custom_objects=None, compile=True)
prediction = new_model.predict([x_test])
print(prediction)
Errors are:
Traceback (most recent call last): File
"C:/Users/TanveerIslam/PycharmProjects/DeepLearningPractice/1.py",
line 32, in
new_model = load_model('124446.model', custom_objects=None, compile=True) File
"C:\Users\TanveerIslam\PycharmProjects\DeepLearningPractice\venv\lib\site-packages\tensorflow\python\keras\engine\saving.py",
line 262, in load_model
sample_weight_mode=sample_weight_mode) File "C:\Users\TanveerIslam\PycharmProjects\DeepLearningPractice\venv\lib\site-packages\tensorflow\python\training\checkpointable\base.py",
line 426, in _method_wrapper
method(self, *args, **kwargs) File "C:\Users\TanveerIslam\PycharmProjects\DeepLearningPractice\venv\lib\site-packages\tensorflow\python\keras\engine\training.py",
line 525, in compile
metrics, self.output_names)
AttributeError: 'Sequential' object has no attribute 'output_names'
So can any one give me ant solution.
Note: I use pycharm as IDE.

As #Shinva said to set the "compile" attribute of the load_model function to "False".
Then after loading the model, compile it separately.
from tensorflow.keras.models import save_model, load_model
save_model(model,'124446.model')
Then for loading the model again do:
saved_model = load_model('124446.model', compile=False)
saved_model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
saved_model.predict([x_test])
Update: For some unknown reasons, I started to get the same errors as the question states. After trying to find different solutions it seems using the "keras" library directly instead of "tensorflow.keras" works properly.
My setup is on "Windows 10" with python:'3.6.7', tensorflow:'1.11.0' and keras:'2.2.4'
As per my knowledge, there are three different ways in which you can save and restore your model; provided you have used keras directly to make your model.
Option1:
import json
from keras.models import model_from_json, load_model
# Save Weights + Architecture
model.save_weights('model_weights.h5')
with open('model_architecture.json', 'w') as f:
f.write(model.to_json())
# Load Weights + Architecture
with open('model_architecture.json', 'r') as f:
new_model = model_from_json(f.read())
new_model.load_weights('model_weights.h5')
Option2:
from keras.models import save_model, load_model
# Creates a HDF5 file 'my_model.h5'
save_model(model, 'my_model.h5') # model, [path + "/"] name of model
# Deletes the existing model
del model
# Returns a compiled model identical to the previous one
new_model = load_model('my_model.h5')
Option 3
# using model's methods
model.save("my_model.h5")
# deletes the existing model
del model
# load the saved model back
new_model = load_model('my_model.h5')
Option 1 requires the new_model to be compiled before using.
Option 2 and 3 are almost similar in syntax.
Codes used from:
1. Saving & Loading Keras Models
2. https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model

I was able to load the model by setting compile=False in load_model()

import tensorflow as tf
tf.keras.models.save_model(
model,
"epic_num_reader.model",
overwrite=True,
include_optimizer=True
)
new_model = tf.keras.models.load_model('epic_num_reader.model', custom_objects=None, compile=False)
predictions = new_model.predict(x_test)
print(predictions)
import numpy as np
print(np.argmax(predictions[0]))
plt.imshow(x_test[0],cmap=plt.cm.binary)
plt.show()

If this is run on Windows then the issue is that currently toco is not supported on Windows - https://github.com/tensorflow/tensorflow/issues/20975