I want to visualize the classification result with tensorflow2.0. For keras, it need the following code for cam:
import tensorflow as tf
import keras.backend as K
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input, decodpredictions
import numpy as np
import cv2
img_path = 'image/test.jpg'
model = VGG16(weights='imagenet')
img = image.load_img('image/test.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
print('Predicted:', decode_predictions(preds, top=3)[0])
print np.argmax(preds[0])
african_elephant_output = model.output[:, 386]
last_conv_layer = model.get_layer('block5_conv3')
grads = K.gradients(african_elephant_output, last_conv_layer.output)[0]
But when I use tensorflow2.0, it seem no such gradient function. So what is the corresponding function for K.gradients for tensorflow2.0?
Here:
import keras.backend as K
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input, decodpredictions
You are mixing the keras and tf.keras packages, which are NOT compatible with each other. You should import backend from tf.keras:
import tensorflow.keras.backend as K
Related
My goal is changing the data inside the model using lambda function
the code fails at the last part in model.predict
can someone help me fix this or give me a similar one if you have?
import glob
import tensorflow as tf
import tensorflow_hub as hub
from os.path import basename, join
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.optimizers import Adam
from keras import layers
from sklearn.metrics import auc, average_precision_score
import numpy as np
import base64
import cv2
#this is the function that i want to call inside the model
#it take th data which in an image encoded base64 and it decode it back into a numpy array
def base64_decoder(inputs):
binary_data = base64.b64decode(inputs)
img = cv2.imdecode(np.frombuffer(binary_data, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
return img
#this is the model i'm using to test
model_handle="https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_s/feature_vector/2"
INPUT_SIZE=(331, 331,3)
model = tf.keras.Sequential([
tf.keras.layers.Lambda(base64_decoder),
tf.keras.layers.InputLayer(input_shape=INPUT_SIZE),
hub.KerasLayer(model_handle, trainable=True),
tf.keras.layers.Dropout(rate=0.2),
layers.Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=[tf.keras.metrics.AUC(curve='PR')])
# now i will test the model
im = np.random.rand(331,331,3)
img = np.expand_dims(im, axis=0)
#encode the random image
_, buffer = cv2.imencode('.jpg', im)
jpg_as_text = base64.b64encode(buffer)
value=jpg_as_text.decode('utf-8')
#predict
print(model.predict(value))
also i may change the lambda function to a full custom function, what do you think?
When trying to use the ConvNeXtTiny model from Keras, I get the following error: AttributeError: module 'keras.applications' has no attribute 'ConvNeXtTiny'
filename = "ConvNextTiny_firstpass_model"
# layer construction
base_model = applications.ConvNeXtTiny( #preproccing included
input_shape=(targetWidth, targetHeight, 3),
include_top=False,
)
base_model.trainable = False
flatten_layer = layers.Flatten()
fc_layer = layers.Dense(1024, activation='relu')
dropout_layer = layers.Dropout(0.3)
#layer connecting
x = flip_layer(input_layer)
x = base_model(x, training=False)
x = flatten_layer(x)
x = fc_layer(x)
x = dropout_layer(x)
predictions = output_layer(x)
model = keras.Model(input_layer, predictions)
Here are my imports:
import tensorflow as tf
import keras
from keras import layers
from keras import optimizers
from keras import applications
from keras import losses
from keras import callbacks
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import cv2 as cv
import csv
from sklearn.utils import shuffle
Possibly relevant versioning:
ipython==8.5.0
tensorflow==2.10.0
keras==2.10.0
Keras-Preprocessing==1.1.2
pandas==1.4.4
numpy==1.23.3
matplotlib==3.6.0
opencv-python==4.6.0.66
sklearn==0.0
The previous imports placed above the convnext import were causing issues.
Moving from tensorflow.keras.applications import convnext to the top of all the imports allowed it to import properly.
I'm working on a webapp for a machine learning project using Streamlit.
I've encountered this error at load_img:
TypeError: expected str, bytes or os.PathLike object, not UploadedFile.
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.applications.vgg16 import decode_predictions
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.models import model_from_json
import numpy as np
import cv2
import tensorflow as tf
import streamlit as st
st.write("This is a simple image classification web app to identify cars")
file = st.file_uploader("Please upload an image file", type=["jpg", "png"])
def import_and_predict(image_data, model):
image = load_img(image_data,target_size=(64,64))
img = img_to_array(image)
img = np.array(image)
img = img / 255.0
image = img.resize((64,64))
img = img.reshape(1,64,64,3)
label = model.predict_classes(img)
prediction = label[0][0]
return f"Prediction: {prediction}"
if file is None:
st.text("Please upload an image file")
else:
import_and_predict(file, model)
From the Streamlit documentation for file_uploader:
>>> uploaded_file = st.file_uploader("Choose a file")
>>> if uploaded_file is not None:
... # To read file as bytes:
... bytes_data = uploaded_file.getvalue()
... st.write(bytes_data)
The above code will give you a BytesIO object, which can then be converted into an array representing the image.
I am working on medical image classification using Resnet50 model. Whenever I try to flatten the layer I am getting this error.
ValueError: Attempt to convert a value (None) with an unsupported type (<class 'NoneType'>) to a Tensor.
My code is as below
from PIL import Image
import numpy as np
import tensorflow
from tensorflow.keras import layers
from tensorflow.keras.callbacks import Callback, ModelCheckpoint, ReduceLROnPlateau, TensorBoard, EarlyStopping
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from keras.utils.np_utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import cohen_kappa_score, accuracy_score
import scipy
from tensorflow.keras import backend as K
import gc
from functools import partial
from tqdm import tqdm
from sklearn import metrics
from collections import Counter
import json
import itertools
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D,GlobalAveragePooling2D
from keras.layers import Input, Lambda, Dense, Flatten
from keras.preprocessing import image
from glob import glob
pre_trained_model = tensorflow.keras.applications.ResNet50(input_shape=(224,224,3), include_top=False, weights="imagenet")
from keras.applications.resnet50 import ResNet50
from keras.models import Model
import keras
restnet = ResNet50(include_top=False, weights='imagenet', input_shape=(224,224,3))
output = restnet.layers[-1].output
output = keras.layers.Flatten()(output)
restnet = Model(restnet.input, output=output)
for layer in restnet.layers:
layer.trainable = False
restnet.summary()
I also tried adding the output layer this way:
last_layer = pre_trained_model.get_layer('conv5_block3_out')
print('last layer output shape:', last_layer.output_shape)
last_output = last_layer.output
x = GlobalAveragePooling2D()(last_output)
x = layers.Dropout(0.5)(x)
x = layers.Dense(3, activation='softmax')(x)
But got this error:
TypeError: Cannot convert a symbolic Keras input/output to a numpy array. This error may indicate that you're trying to pass a symbolic value to a NumPy call, which is not supported. Or, you may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model.
I am unable to understand both errors, checked the soln given here, but that didn't solve my problem.
You are mixing tensorflow and keras libraries. Recommended to use only tensorflow.keras.* instead of keras.*.
Here is the modified code:
from PIL import Image
import numpy as np
import tensorflow
from tensorflow.keras import layers
from tensorflow.keras.callbacks import Callback, ModelCheckpoint, ReduceLROnPlateau, TensorBoard, EarlyStopping
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from keras.utils.np_utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import cohen_kappa_score, accuracy_score
import scipy
from tensorflow.keras import backend as K
import gc
from functools import partial
from tqdm import tqdm
from sklearn import metrics
from collections import Counter
import json
import itertools
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D,GlobalAveragePooling2D
from tensorflow.keras.layers import Input, Lambda, Dense, Flatten
from tensorflow.keras.preprocessing import image
from glob import glob
pre_trained_model = tensorflow.keras.applications.ResNet50(input_shape=(224,224,3), include_top=False, weights="imagenet")
from keras.applications.resnet50 import ResNet50
from keras.models import Model
import keras
restnet = ResNet50(include_top=False, weights='imagenet', input_shape=(224,224,3))
output = restnet.layers[-1].output
output = tensorflow.keras.layers.Flatten()(output)
restnet = tensorflow.keras.models.Model(restnet.input, outputs=output)
for layer in restnet.layers:
layer.trainable = False
restnet.summary()
I am trying to train EfficientNetB1 on Google Colab and constantly running into different issues with correct import statements from Keras or Tensorflow.Keras, currently this is how my imports look like
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.layers.pooling import AveragePooling2D
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import SGD
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import argparse
import pickle
import cv2
import os
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
import efficientnet.keras as enet
from tensorflow.keras.layers import Dense, Dropout, Activation, BatchNormalization, Flatten, Input
and this is how my model looks like
load the ResNet-50 network, ensuring the head FC layer sets are left
# off
baseModel = enet.EfficientNetB1(weights="imagenet", include_top=False, input_tensor=Input(shape=(224, 224, 3)), pooling='avg')
# Adding 2 fully-connected layers to B0.
x = baseModel.output
x = BatchNormalization()(x)
x = Dropout(0.7)(x)
x = Dense(512)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dropout(0.5)(x)
x = Dense(512)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# Output layer
predictions = Dense(len(lb.classes_), activation="softmax")(x)
model = Model(inputs = baseModel.input, outputs = predictions)
# loop over all layers in the base model and freeze them so they will
# *not* be updated during the training process
for layer in baseModel.layers:
layer.trainable = False
But for the life of me I can't figure out why I am getting the below error
AttributeError Traceback (most recent call last)
<ipython-input-19-269fe6fc6f99> in <module>()
----> 1 baseModel = enet.EfficientNetB1(weights="imagenet", include_top=False, input_tensor=Input(shape=(224, 224, 3)), pooling='avg')
2
3 # Adding 2 fully-connected layers to B0.
4 x = baseModel.output
5 x = BatchNormalization()(x)
5 frames
/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in _collect_previous_mask(input_tensors)
1439 inbound_layer, node_index, tensor_index = x._keras_history
1440 node = inbound_layer._inbound_nodes[node_index]
-> 1441 mask = node.output_masks[tensor_index]
1442 masks.append(mask)
1443 else:
AttributeError: 'Node' object has no attribute 'output_masks'
The problem is the way you import the efficientnet.
You import it from the Keras package and not from the TensorFlow.Keras package.
Change your efficientnet import to
import efficientnet.tfkeras as enet
Not sure, but this error maybe caused by wrong TF version. Google Colab for now comes with TF 1.x by default. Try this to change the TF version and see if this resolves the issue.
try:
%tensorflow_version 2.x
except:
print("Failed to load")