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.
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?
I'm trying to do transfer learning with MobileNetV3 in Keras but I'm having some issues.
from keras.models import Model
from keras.layers import GlobalMaxPooling2D, Dense, Dropout
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from tensorflow.keras.applications import MobileNetV3Small
import numpy as np
from tqdm import tqdm
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
pretrained_model = MobileNetV3Small(input_shape=(224,224,3),
weights="imagenet",
include_top=False)
# freeze all layers except the last one
for layer in pretrained_model.layers:
layer.trainable = False
pretrained_model.layers[-1].trainable = True
# combine the model with some extra layers for classification
last_output = pretrained_model.layers[-1].output
x = GlobalMaxPooling2D()(last_output)
x = Dense(128, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(1, activation='sigmoid')(x)
model = Model(pretrained_model.input, x)
I get this error when I try to make the Dense layer:
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.
but it's fixed by adding the following code snippet:
from tensorflow.python.framework.ops import disable_eager_execution
disable_eager_execution()
When I include the code fix above, I get this error when I call model.fit():
FailedPreconditionError: 2 root error(s) found.
(0) Failed precondition: Could not find variable Conv_1_2/kernel. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status=Not found: Resource localhost/Conv_1_2/kernel/N10tensorflow3VarE does not exist.
[[{{node Conv_1_2/Conv2D/ReadVariableOp}}]]
[[_arg_dense_12_target_0_1/_100]]
(1) Failed precondition: Could not find variable Conv_1_2/kernel. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status=Not found: Resource localhost/Conv_1_2/kernel/N10tensorflow3VarE does not exist.
[[{{node Conv_1_2/Conv2D/ReadVariableOp}}]]
0 successful operations.
0 derived errors ignored.
How can I fix these issues and train the model?
From comments
Don't mix tf.keras and standalone keras. They are not compatible. Only use one of them (paraphrased from Frightera)
Working code as shown below
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalMaxPooling2D, Dense, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.applications import MobileNetV3Small
import numpy as np
from tqdm import tqdm
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
pretrained_model = MobileNetV3Small(input_shape=(224,224,3),
weights="imagenet",
include_top=False)
# freeze all layers except the last one
for layer in pretrained_model.layers:
layer.trainable = False
pretrained_model.layers[-1].trainable = True
# combine the model with some extra layers for classification
last_output = pretrained_model.layers[-1].output
x = GlobalMaxPooling2D()(last_output)
x = Dense(128, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(1, activation='sigmoid')(x)
model = Model(pretrained_model.input, x)
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")
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
I am using keras 1.2 with tensorflow 1.0.0 backend.
I have a function that loads a pre-calibrated model from json and then loads its weights from a hdf5 file.
def load():
model = model_from_json(open(model_path).read())
model.load_weights(model_weights_path)
This function, more precisely the call to load_weights results in the following exception:
RuntimeError: The Session graph is empty. Add operations to the graph before calling run()
I wonder if that is due to these lines that I put in the beginning of my module to set the tensorflow seed for reproducibility:
tf.set_random_seed(123) # To set Tensorflow seed
sess = tf.Session()
keras.backend.set_session(sess)
It seems the keras session does not automatically set the loaded model as the graph associated to the session, hence failing to initialize the weights.
Any explanation and workaround to avoid the exception?
I pretty much am using the same code as you and it works for me.
from keras.models import Sequential
from keras.models import Model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, GlobalAveragePooling2D
from keras.optimizers import RMSprop
from keras.utils import np_utils
from keras.models import model_from_json
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers.pooling import AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import ZeroPadding2D
from keras.engine.topology import Merge
from keras.layers import merge
from keras.optimizers import Adam
from keras import backend as K
from keras.layers.pooling import MaxPooling2D
from keras.layers.convolutional import ZeroPadding2D
import PIL
import inception
import tensorflow as tf
import keras
import glob
import pandas as pd
import pickle
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
# load json and create model
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights("model.h5")
print("Loaded model from disk")
model.summary()
model.compile(Adam(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
score = model.predict(transfer_values_test)
Indeed it seems that Keras doesn't respect the session set by set_session when loading models.
Try forcing Keras to use a particular session by Tensorflow's context manager:
def load():
with sess.as_default():
model = model_from_json(open(model_path).read())
model.load_weights(model_weights_path)''
If Keras still complains, predefine a graph (graph=tf.Graph()) and force model.load_weights to use it by introducing an additional with statement:
def load():
with graph.as_default():
with sess.as_default():
model = model_from_json(open(model_path).read())
model.load_weights(model_weights_path)''