I have an already trained neuronal network consisting of files NNbiases_b1.csv, NNbiases_out.csv, NNweights_h1.csv and NNweights_out.csv. The input and output layer sizes are known too.
Now I'm looking for a Python script that uses this neuronal network, means outputs data dependent on input data and trained network.
But whenever I google for an related script, I only find howtos and explanations about training an network!
So my question: when I have an already trained network with the data/files above: how can I use this neuronal network?
Thanks!
I think you need to reconstruct your model's architecture, and then manually set the weights of each layer with something like that :
all_weights = []
NNweights_h1 = [...] #load your csv of weights
NNbiases_b1 = [...] #load your csv of biases
all_weights.append(NNweights_h1)
all_weights.append(NNbiases_b1)
model.layers[i].set_weights(all_weights)
And do that for all your layers.
Update after precisions
In order to use your model (dummy exemple) :
Reconstruct the architecture :
def model(model_input):
x = Dense(12, input_dim=8, activation='relu')(model_input)
x = Dense(1, activation='sigmoid')(x)
model = Model(model_input, x, name='Your_model')
return model
Instanciate it :
X_test = [...] #load your data
input_shape = [...] #your test data shape
model_input = Input(shape=input_shape)
model = model(model_input)
Manually set the weights with using the code at the begining of the answer
Use this model to predict your data:
prediction = model.predict(X_test) #get the predictions of your model
I hope this will help you !
Related
I am developing an autoencoder for clustering certain groups of images.
input_images->...->bottleneck->...->output_images
I have calibrated the autoencoder to my satisfaction and saved the model; everything has been developed using keras.tensorflow on python3.
The next step is to apply the autoencoder to a ton of images and cluster them according to cosine distance in the bottleneck layer. Oops, I just realized that I don't know the syntax in keras.tf for running the model on a batch up to a specific layer rather than to the output layer. Thus the question:
How do I run something like Model.predict_on_batch or Model.predict_generator up to the certain "bottleneck" layer and retrieve the values on that layer rather than the values on the output layer?
You need to define a new model (if you didn't define the encoder and decoder as separate models initially, which is usually the easiest option).
If your model was defined without reusing layers, it's just:
inputs = model.input
outputs= model.get_layer('bottleneck').output
encoder = Model(inputs, outputs)
Use the encoder model as any other model.
The full code would be like this,
# ENCODER
encoding_dim = 37310
input_layer = Input(shape=(encoding_dim,))
encoder = Dense(500, activation='tanh')(input_layer)
encoder = Dense(100, activation='tanh')(encoder)
encoder = Dense(50, activation='tanh', name='bottleneck_layer')(encoder)
decoder = Dense(100, activation='tanh')(encoder)
decoder = Dense(500, activation='tanh')(decoder)
decoder = Dense(37310, activation='sigmoid')(decoder)
# full model
model_full = models.Model(input_layer, decoder)
model_full.compile(optimizer='adam', loss='mse')
model_full.fit(x, y, epochs=20, batch_size=16)
# bottleneck model
bottleneck_output = model_full.get_layer('bottleneck_layer').output
model_bottleneck = models.Model(inputs = model_full.input, outputs = bottleneck_output)
bottleneck_predictions = model_bottleneck.predict(X_test)
I have a tensorflow keras model trained with tensorflow 2.3. The model takes as input an image, however the model was trained with scaled inputs and therefore we have to scale the image by 255 before inputting them into the model.
As we use this model across a variety of platforms, I am trying to simplify this by modifying the model to simply insert a rescale layer at the start of the keras model (i.e. immediately after the input). Therefore any future consumption of this model can simply pass an image without having to scale them.
I am having a lot of trouble getting this to work. I understand I need to use the following function to create a rescaling layer;
tf.keras.layers.experimental.preprocessing.Rescaling(255, 0.0, "rescaling")
But I am unsure how to insert this to the start of the model.
Thank you in advance
you can insert this layer at the top of your trained model. below an example where first we train a model manual scaling the input and the we using the same trained model but adding at the top a Rescaling layer
from tensorflow.keras.layers.experimental.preprocessing import Rescaling
# generate dummy data
input_dim = (28,28,3)
n_sample = 10
X = np.random.randint(0,255, (n_sample,)+input_dim)
y = np.random.uniform(0,1, (n_sample,))
# create base model
inp = Input(input_dim)
x = Conv2D(8, (3,3))(inp)
x = Flatten()(x)
out = Dense(1)(x)
# fit base model with manual scaling
model = Model(inp, out)
model.compile('adam', 'mse')
model.fit(X/255, y, epochs=3)
# create new model with pretrained weight + rescaling at the top
inp = Input(input_dim)
scaled_input = Rescaling(1/255, 0.0, "rescaling")(inp)
out = model(scaled_input)
scaled_model = Model(inp, out)
# compare prediction with manual scaling vs layer scaling
pred = model.predict(X/255)
pred_scaled = scaled_model.predict(X)
(pred.round(5) == pred_scaled.round(5)).all() # True
Rescaling the images is part of data preprocessing, also rescaling images is called image normalization, this process is useful for providing a uniform scale for the dataset or numerical values you are using before building your model.In keras you can do this in many ways using one of the following according to your target:
If you are training using an Artificial neural network model you can use:-
"Batch normalization layer" or "Layer Normalization" or by the rescale method of keras you mentioned. You can look at this resource for more information about normalization .
https://machinelearningknowledge.ai/keras-normalization-layers-explained-for-beginners-batch-normalization-vs-layer-normalization/
to use the rescale method you mentioned:
#importing you libraries 1st
import tensorflow as tf
from tensorflow.keras.layers import BatchNormalization
#if your are using dataset from directory
import pathlib
then import your Dataset:
Dataset_Dir = '/Dataset/ path'
image size = (256,256) #the image size in your dataset
image shape = (96,96,3) #The shape you wish for your images in your network
Then divide your dataset to train-test I use 70-30 percent
Training_set = tf.keras.preprocessing.image_dataset_from_directory(Dataset_Dir,batch_size= 32,
image_size= image_size,
validation_split= 0.3,subset = "training",seed =123)
Test set
Testing_set = tf.keras.preprocessing.image_dataset_from_directory(Dataset_Dir,image_size= image_size,
validation_split=0.3,seed=123,subset ="validation")
normalization layer:
normalization_layer = tf.keras.layers.experimental.preprocessing.Rescaling(1./255)
normalized_training_set = Training_set.map(lambda x, y: (normalization_layer(x), y))
training_image_batch,training_labels_batch = next(iter(normalized_training_set))
for more about this method too:
look at tensorflow tutorial:
https://www.tensorflow.org/tutorials/images/classification
I have a CNN model trained using EfficientNetB6.
My task is to extract the features of this trained model by removing the last dense layer and then using those weights to train a boosting model.
i did this using Pytorch earlier and was able to extract the weights from the layers i was interested and predicted on my validation set and then boosted.
I am doing this now in tensorflow but currently stuck.
Below is my model structure and I have tried using the code on the website but did not had any luck.
I want to remove the last dense layer and predict on the validation set using the remaining layers.
I tried using :
layer_name = 'efficientnet-b6'
intermediate_layer_model = tf.keras.Model(inputs = model.input, outputs = model.get_layer(layer_name).output)
but i get an error "
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_1:0", shape=(None, 760, 760, 3), dtype=float32) at layer "input_1". The following previous layers were accessed without issue: []"
Any way to resolve this?
Sorry my bad.
I simply added a GlobalAveragePooling2D layer after the efficientnet layer and i am able to extract the weights and continue :)
just for reference:
def build_model(dim=CFG['net_size'], ef=0):
inp = tf.keras.layers.Input(shape=(dim,dim,3))
base = EFNS[ef](input_shape=(dim,dim,3),weights='imagenet',include_top=False)
x = base(inp)
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(1,activation='sigmoid')(x)
model = tf.keras.Model(inputs=inp,outputs=x)
opt = tf.keras.optimizers.Adam(learning_rate=0.001)
loss = tf.keras.losses.BinaryCrossentropy(label_smoothing=0.05)
model.compile(optimizer=CFG['optimizer'],loss=loss,metrics=[tf.keras.metrics.AUC(name='auc')])
print(model.summary())
return model
Here's the kind of model I want to make
I'm trying to make a sort of adversarial network. I have already trained a discriminator. Now I want to connect outputs of two separate VGG16 models to this discriminator. I'm trying to avoid retraining the discriminator and just simply load pre-trained weights, that is why I want to load the model and then somehow connect it with the two VGG16 models.
If there is a way I can load the weights for the discriminator part only somehow that would be great as well!
Apologies if my query is not in line with the guidelines, this is my second every post on stackoverflow.
EDIT: Here's the code I've written for the overall structure without loading weights, i.e. I will be training this from scratch.
sat/street extractors are VGG16 networks that learn features
sat_fv = sat_extractor.layers[-1].output
str_fv = street_extractor.layers[-1].output
deep_sat_feat = Reshape((32, 32, 1))(sat_fv)
deep_street_feat = Reshape((32, 32, 1))(str_fv)
deep_merged = concatenate([deep_sat_feat, deep_street_feat], axis=3)
deep_act_1 = conv2d_block(deep_merged)
flat = Flatten()(deep_act_1)
dense = Dense(4096)(flat)
act = LeakyReLU()(dense)
dense = Dense(1024)(act)
act = LeakyReLU()(dense)
dense = Dense(128)(act)
act = LeakyReLU()(dense)
den_3 = Dense(1)(act)
act_5 = Activation("sigmoid")(den_3)
generator_model = Model(inputs=[sat_extractor.input,street_extractor.input], outputs=[sat_fv,str_fv,act_5])
discriminator_model = Model(inputs=[sat_extractor.input,street_extractor.input], outputs=[act_5])
generator_model.compile('Adam',loss=['categorical_crossentropy','categorical_crossentropy','binary_crossentropy'],loss_weights=[1/15,1/15,1/3])
discriminator_model.compile(loss="binary_crossentropy", optimizer=Adam(0.00001), metrics=["accuracy"])
I have a Sequential Model built in Keras and after trained it give me good prediction but when i save and then load the model i don't obtain the same prediction on the same dataset. Why?
Note that I checked the weight of the model and they are the same as well as the architecture of the model, checked with model.summary() and model.getWeights(). This is very strange in my opinion and I have no idea how to deal with this problem.
I don't have any error but the prediction are different
I tried to use model.save() and load_model()
I tried to use model.save_weights() and after that re-built the model and then load the model
I have the same problem with both options.
def Classifier(input_shape, word_to_vec_map, word_to_index, emb_dim, num_activation):
sentence_indices = Input(shape=input_shape, dtype=np.int32)
emb_dim = 300 # embedding di 300 parole in italiano
embedding_layer = pretrained_embedding_layer(word_to_vec_map, word_to_index, emb_dim)
embeddings = embedding_layer(sentence_indices)
X = LSTM(256, return_sequences=True)(embeddings)
X = Dropout(0.15)(X)
X = LSTM(128)(X)
X = Dropout(0.15)(X)
X = Dense(num_activation, activation='softmax')(X)
model = Model(sentence_indices, X)
sequentialModel = Sequential(model.layers)
return sequentialModel
model = Classifier((maxLen,), word_to_vec_map, word_to_index, maxLen, num_activation)
...
model.fit(Y_train_indices, Z_train_oh, epochs=30, batch_size=32, shuffle=True)
# attempt 1
model.save('classificationTest.h5', True, True)
modelRNN = load_model(r'C:\Users\Alessio\classificationTest.h5')
# attempt 2
model.save_weights("myWeight.h5")
model = Classifier((maxLen,), word_to_vec_map, word_to_index, maxLen, num_activation)
model.load_weights(r'C:\Users\Alessio\myWeight.h5')
# PREDICTION TEST
code_train, category_train, category_code_train, text_train = read_csv_for_email(r'C:\Users\Alessio\Desktop\6Febbraio\2test.csv')
categories, code_categories = get_categories(r'C:\Users\Alessio\Desktop\6Febbraio\2test.csv')
X_my_sentences = text_train
Y_my_labels = category_code_train
X_test_indices = sentences_to_indices(X_my_sentences, word_to_index, maxLen)
pred = model.predict(X_test_indices)
def codeToCategory(categories, code_categories, current_code):
i = 0;
for code in code_categories:
if code == current_code:
return categories[i]
i = i + 1
return "no_one_find"
# result
for i in range(len(Y_my_labels)):
num = np.argmax(pred[i])
# Pretrained embedding layer
def pretrained_embedding_layer(word_to_vec_map, word_to_index, emb_dim):
"""
Creates a Keras Embedding() layer and loads in pre-trained GloVe 50-dimensional vectors.
Arguments:
word_to_vec_map -- dictionary mapping words to their GloVe vector representation.
word_to_index -- dictionary mapping from words to their indices in the vocabulary (400,001 words)
Returns:
embedding_layer -- pretrained layer Keras instance
"""
vocab_len = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)
### START CODE HERE ###
# Initialize the embedding matrix as a numpy array of zeros of shape (vocab_len, dimensions of word vectors = emb_dim)
emb_matrix = np.zeros((vocab_len, emb_dim))
# Set each row "index" of the embedding matrix to be the word vector representation of the "index"th word of the vocabulary
for word, index in word_to_index.items():
emb_matrix[index, :] = word_to_vec_map[word]
# Define Keras embedding layer with the correct output/input sizes, make it trainable. Use Embedding(...). Make sure to set trainable=False.
embedding_layer = Embedding(vocab_len, emb_dim)
### END CODE HERE ###
# Build the embedding layer, it is required before setting the weights of the embedding layer. Do not modify the "None".
embedding_layer.build((None,))
# Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.
embedding_layer.set_weights([emb_matrix])
return embedding_layer
Do you have any kind of suggestion?
Thanks in Advance.
Edit1: if use the code of saving and loading in the same "page" (I'm using notebook jupyter) it works fine. If I change "page" it doesn't work. Could it be that there is something related with the tensorflow session?
Edit2: my final goal is to load a model, trained in Keras, with Deeplearning4J in java. So if you know a solution for "transforming" the keras model in something else readable in DL4J it will help anyway.
Edit3: add function pretrained_embedding_layer()
Edit4: dictionaries from word2Vec model read with gensim
from gensim.models import Word2Vec
model = Word2Vec.load('C:/Users/Alessio/Desktop/emoji_ita/embedding/glove_WIKI')
def getMyModels (model):
word_to_index = dict({})
index_to_word = dict({})
word_to_vec_map = dict({})
for idx, key in enumerate(model.wv.vocab):
word_to_index[key] = idx
index_to_word[idx] = key
word_to_vec_map[key] = model.wv[key]
return word_to_index, index_to_word, word_to_vec_map
Are you pre-processing your data in the same way when you load your model ?
And if yes, did you set the seed of your pre-processing functions ?
If you build a dictionnary with keras, are the sentences coming in the same order ?
I had the same problem before, so here is how you solve it. After making sure that the weights and summary are the same, try to print your random seed and check. If its value is changing from a session to another and if you tried tensorflow's seed, it means you need to disable the PYTHONHASHSEED environment variable. You can read more about it here:
https://docs.python.org/3/using/cmdline.html#envvar-PYTHONHASHSEED
To disable it, go to your system environment variables and add PYTHONHASHSEED as a new variable if it doesn't exist. Then, set its value to 0 to disable it. Please note that it was done in this way because it has to be disabled before running the interpreter.