Hi I followed a forum from Github about removing the last layers of a pre-trained model. However it's not working for me; perhaps I did something wrong
I'm following this and here's my code. I thought all I had to do was model.layers[-2].output but it's telling me AttributeError: 'Tensor' object has no attribute 'summary'
import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.backend as K
import numpy as np
from tensorflow.keras.layers import Dense, Input, Layer
from tensorflow.keras.models import Model
from tensorflow.keras.applications.inception_v3 import InceptionV3
model = InceptionV3()
print(model.summary())
modele = model.layers[-2].output
print(modele.summary())
modele variable is only a layer. You have to do:
model = InceptionV3()
print(model.summary())
output = model.layers[-2].output
modele = Model(inputs = model.input, outputs = output)
print(modele.summary())
Related
I'm having a hard time saving keras model and loading it.
It continues to raise the: AttributeError: 'str' object has no attribute 'decode'
when I load_model()
import numpy as np
import pandas as pd
from keras.utils import to_categorical
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import LSTM, Dense, GRU, Embedding
from keras.callbacks import EarlyStopping, ModelCheckpoint
activation='softmax'
loss ='categorical_crossentropy'
optimizer ='adam'
epochs = 2
# generate model
model = Sequential()
model.add(Embedding(vocab, 100, input_length=n, trainable=True))
model.add(GRU(150, recurrent_dropout=0.1, dropout=0.1))
model.add(Dense(vocab, activation=activation))
print(model.summary())
# compile the model
model.compile(loss=loss, metrics=['acc'], optimizer=optimizer)
# fit the model
model.fit(X_tr, y_tr, epochs=epochs, verbose=2, validation_data=(X_val, y_val))
# saving model
model.save("model_test.h5")
# load model
from keras.models import load_model
# load model
new_model = tf.keras.models.load_model("model_test")
returns: AttributeError: 'str' object has no attribute 'decode'
Try the below code and let me know if this works or not!
# load model package
from keras.models import load_model
# load model
new_model = load_model("model_test.h5")
import keras
print(keras.__version__)
#2.3.0
from keras.models import Sequential
from keras.layers import Input, Dense,TimeDistributed
from keras.models import Model
model = Sequential()
resnet = ResNet50(include_top = False, pooling = 'avg', weights = 'imagenet')
model.add(resnet)
model.add(Dense(10, activation = 'relu'))
model.add(Dense(6, activation = 'sigmoid'))
model.summary()
// Training // model.fit( .. ) done
now how to just the output from layer ?
model.layers[0]._name='resnet50'
print(model.layers[0].name) # prints resnet50
layer_output = model.get_layer("resnet50").output
intermediate_model = Model(inputs=[model.input, resnet.input], outputs=[layer_output])
result = intermediate_model.predict([x, x])
print(result.shape)
print(result[0].shape)
Got Error
AttributeError: Layer resnet50 has multiple inbound nodes, hence the
notion of "layer output" is ill-defined. Use
get_output_at(node_index) instead. add Codeadd Markdown
Please try again using tf.keras to import model and layers.
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Dense,TimeDistributed
from tensorflow.keras.models import Model
and then run the same:
model.layers[0]._name='resnet50'
print(model.layers[0].name) # prints resnet50
layer_output = model.get_layer("resnet50").output
intermediate_model = Model(inputs=[model.input, resnet.input], outputs=[layer_output])
x = tf.ones((1, 250, 250, 3))
result = intermediate_model.predict([x, x])
print(result.shape)
print(result[0].shape)
Output:
resnet50
(1, 2048)
(2048,)
Keras model's validation accuracy is not changing, and when I try to run the model it's just outputting same messy numbers. As input I'm giving it flatten 5x4 array of bounding boxes, and the output are keypresses (W,A,S,D).
I'm using it to avoid detected objects for autopilot.
I tried changing activation, layers in model, learning rate and so on, but nothing helped.
Any help would be appreciated.
from tensorflow import keras
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, Input
from tensorflow.keras.callbacks import TensorBoard
data = np.load("training_data/data.npz",allow_pickle=True)
X = data['arr_2']
X = keras.utils.normalize(X,axis=1)
Y = data['arr_1']
# Creating model for object processing
model2 = Sequential()
# Model for object processing
# Inputs
model2.add(Input(shape=(20)))
# Hidden layer
model2.add(Dense(16,activation="relu"))
# Outputs
model2.add(Dense(4,activation="softmax"))
model2.compile(loss='binary_crossentropy',
optimizer="adam",
metrics=['accuracy'])
model2.fit(X,Y,batch_size=256, epochs=20, validation_split=0.1)
I'm trying to search Hyperparameters for a model using Keras Tuner, but I'm getting this error when I run the code: "RuntimeError: Model-building function did not return a valid Keras Model instance, found < keras.engine.sequential.Sequential object at 0x000001E9C2903F28 >"
I've searched on Internet but didn't found anything that could help, also I've followed the tutorial in the Keras Tuner gitHub page (https://github.com/keras-team/keras-tuner), but it dind't work either.
Here is my code:
class MyHyperModel(HyperModel):
def __init__(self, num_classes):
self.num_classes = num_classes
def build(self, hp):
model=Sequential()
model.add(Dense(units=hp.Int('units_0', 30, 900, step=30),
activation=hp.Choice('act_0', ['relu', 'tanh']),
input_dim=12))
for i in range(hp.Int('layers', 3, 9)):
model.add(Dense(units=hp.Int('units_' + str(i), 30, 900, step=30),
activation=hp.Choice('act_' + str(i), ['relu', 'tanh'])))
model.add(Dense(6, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=hp.Choice('optimizer', ['adam', 'sgd']),
metrics=['categorical_accuracy'])
return model
hypermodel = MyHyperModel(num_classes=6)
tuner = kt.tuners.bayesian.BayesianOptimization(
hypermodel,
objective='val_accuracy',
max_trials=5,
executions_per_trial=3,
seed=(np.random.seed(1)),
directory='Tests',
project_name='test')
tuner.search_space_summary()
tuner.search(data[:200], labels[:200],
verbose=2,
epochs=3,
validation_data=(data[200:], labels[200:]))
models = tuner.get_best_models(num_models=2).summary()
tuner.get_best_hyperparameters()[0].values
tuner.results_summary()
data is an list of 300 vector with 12 values and on labels there are 6 classes which was converted to tensor with the function tensorflow.convert_to_tensor().
I appreciate any help.
If you import module members from keras, you must import from tensorflow.keras instead of keras. For example, if you write:
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.optimizers import Adam
Then change them to:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import Adam
I know what's wrong and is not the code, my model have 6 neurons on the last layer and I've used the loss as 'categorical_crossentropy', but this only works when the labels are 0 and 1, so I've changed the loss to 'sparse_categorical_crossentropy' and metrics to 'accuracy' and it worked.
Thanks everyone for the reply, I appreciate the help.
I have been trying to create a multi-input model using Keras, but got errors. The idea is to combine the text and corresonding topics to make predictions for sentiments. Here's the code:
import numpy as np
text = np.random.randint(5000, size=(442702, 200), dtype='int32')
topic = np.random.randint(2, size=(442702, 227), dtype='int32')
sentiment = to_categorical(np.random.randint(5, size=442702), dtype='int32')
from keras.models import Sequential
from keras.layers import Dense, Activation, Embedding, Flatten, GlobalMaxPool1D, Dropout, Conv1D
from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
from keras.losses import binary_crossentropy
from keras.optimizers import Adam
text_input = Input(shape=(200,), dtype='int32', name='text')
text_encoded = Embedding(input_dim=5000, output_dim=20, input_length=200)(text_input)
text_encoded = Dropout(0.1)(text_encoded)
text_encoded = Conv1D(300, 3, padding='valid', activation='relu', strides=1)(text_encoded)
text_encoded = GlobalMaxPool1D()(text_encoded)
topic_input = Input(shape=(227,), dtype='int32', name='topic')
concatenated = concatenate([text_encoded, topic_input])
sentiment = Dense(5, activation='softmax')(concatenated)
model = Model(inputs=[text_encoded, topic_input], outputs=sentiment)
# summarize layers
print(model.summary())
# plot graph
plot_model(model)
However, this gives me the below error:
TypeError: Tensors in list passed to 'values' of 'ConcatV2' Op have types [float32, int32] that don't all match.
Now if I change dtype of topic_input from 'int32' to 'float32', I got a different error:
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("text_37:0", shape=(?, 200), dtype=int32) at layer "text". The following previous layers were accessed without issue: []
On the other hand, part of the model works just fine with the sequential API.
model = Sequential()
model.add(Embedding(5000, 20, input_length=200))
model.add(Dropout(0.1))
model.add(Conv1D(300, 3, padding='valid', activation='relu', strides=1))
model.add(GlobalMaxPool1D())
model.add(Dense(227))
model.add(Activation('sigmoid'))
print(model.summary())
Any pointers are highly appreciated.
There are few issues with your Keras functional API implementation,
You should use the Concatenate layer as Concatenate(axis=-1)([text_encoded, topic_input]).
In the concatenate layer you are trying to combine an int32 tensor and a float32 tensor, which is not allowed. What you should do is, from keras.backend import cast and concatenated = Concatenate(axis=-1)([text_encoded, cast(topic_input, 'float32')]).
You got variable conflicts, there are two sentiment variables, one pointing to a to_categorical output and the other the output of the final Dense layer.
Your model inputs cannot be intermediate tensors like text_encoded. They should come from Input layers.
To help with your implementation, here's a working version of your code (I am not sure if this is exactly what you wanted though) in TF 1.13.
from keras.utils import to_categorical
text = np.random.randint(5000, size=(442702, 200), dtype='int32')
topic = np.random.randint(2, size=(442702, 227), dtype='int32')
sentiment1 = to_categorical(np.random.randint(5, size=442702), dtype='int32')
from keras.models import Sequential
from keras.layers import Input, Dense, Activation, Embedding, Flatten, GlobalMaxPool1D, Dropout, Conv1D, Concatenate, Lambda
from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
from keras.losses import binary_crossentropy
from keras.optimizers import Adam
from keras.backend import cast
from keras.models import Model
text_input = Input(shape=(200,), dtype='int32', name='text')
text_encoded = Embedding(input_dim=5000, output_dim=20, input_length=200)(text_input)
text_encoded = Dropout(0.1)(text_encoded)
text_encoded = Conv1D(300, 3, padding='valid', activation='relu', strides=1)(text_encoded)
text_encoded = GlobalMaxPool1D()(text_encoded)
topic_input = Input(shape=(227,), dtype='int32', name='topic')
topic_float = Lambda(lambda x:cast(x, 'float32'), name='Floatconverter')(topic_input)
concatenated = Concatenate(axis=-1)([text_encoded, topic_float])
sentiment = Dense(5, activation='softmax')(concatenated)
model = Model(inputs=[text_input, topic_input], outputs=sentiment)
# summarize layers
print(model.summary())
Hope these help.