I performed a simple Quantization Aware Training with Tensorflow on MNIST as follows:
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.datasets import mnist
# Load MNIST dataset
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# Normalize the input image so that each pixel value is between 0 to 1.
train_images = train_images / 255.0
test_images = test_images / 255.0
# Define the model architecture.
model = keras.Sequential([
keras.layers.InputLayer(input_shape=(28, 28)),
keras.layers.Reshape(target_shape=(28, 28, 1)),
keras.layers.Conv2D(filters=12, kernel_size=(3, 3)),
keras.layers.Activation('relu'),
keras.layers.MaxPooling2D(pool_size=(2, 2)),
keras.layers.Flatten(),
keras.layers.Dense(10)
])
# Train the digit classification model
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.fit(
train_images,
train_labels,
epochs=5,
validation_split=0.1,
)
import tensorflow_model_optimization as tfmot
quantize_model = tfmot.quantization.keras.quantize_model
# q_aware stands for for quantization aware.
q_aware_model = quantize_model(model)
# `quantize_model` requires a recompile.
q_aware_model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
train_images_subset = train_images[0:1000] # out of 60000
train_labels_subset = train_labels[0:1000]
q_aware_model.fit(train_images_subset, train_labels_subset,
batch_size=500, epochs=5, validation_split=0.1)
However, when I try to investigate the weights of the quantized model using, for instance, q_aware_model.get_weights()[5], I get an array of type Float-32. I am supposed to get type 8-bit integer; what am I doing wrong?
Related
I am currently studying how CNNs can be used in text classification and found some code on stack overflow that had worked with the use of a keras embedding layer.
I ran the code with the keras embedding but now want to test out what would happen with a pre-trained embedding, I have downloaded the word2vec api from gensim but dont know how to adapt the code from there?
My question is how can I replace the keras embedding layer with a pre-trained embedding like the word2vec model or Glove?
heres is the code
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM, Convolution1D, Flatten, Dropout
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.callbacks import TensorBoard
# Using keras to load the dataset with the top_words
top_words = 10000
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
# Pad the sequence to the same length
max_review_length = 1600
X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)
# Using embedding from Keras
embedding_vecor_length = 300
model = Sequential()
model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))
# Convolutional model (3x conv, flatten, 2x dense)
model.add(Convolution1D(64, 3, padding='same'))
model.add(Convolution1D(32, 3, padding='same'))
model.add(Convolution1D(16, 3, padding='same'))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(180,activation='sigmoid'))
model.add(Dropout(0.2))
model.add(Dense(1,activation='sigmoid'))
# Log to tensorboard
tensorBoardCallback = TensorBoard(log_dir='./logs', write_graph=True)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=3, callbacks=[tensorBoardCallback], batch_size=64)
# Evaluation on the test set
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))
This reads the text file containing the weights, stores the words and their weights in a dictionary, then maps them into a new matrix using the vocabulary of your fit tokenizer.
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM, Convolution1D, Flatten, Dropout
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.callbacks import TensorBoard
from tensorflow import keras
import itertools
import numpy as np
# Using keras to load the dataset with the top_words
top_words = 10000
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
word_index = keras.datasets.imdb.get_word_index()
embedding_vecor_length = 300 # same as the embeds to be loaded below
embeddings_dictionary = dict()
glove_file = open('./embeds/glove.6B.300d.txt', 'rb')
for line in glove_file:
records = line.split() # seperates each line by a white space
word = records[0] # the first element is the word
vector_dimensions = np.asarray(
records[1:], dtype='float32') # the rest are the weights
# storing in dictionary
embeddings_dictionary[word] = vector_dimensions
glove_file.close()
# len_of_vocab = len(word_index)
embeddings_matrix = np.zeros((top_words, embedding_vecor_length))
# mapping to a new matrix, using only the words in your tokenizer's vocabulary
for word, index in word_index.items():
if index>=top_words:
continue
# the weights of the individual words in your vocabulary
embedding_vector = embeddings_dictionary.get(bytes(word, 'utf-8'))
if embedding_vector is not None:
embeddings_matrix[index] = embedding_vector
# Pad the sequence to the same length
max_review_length = 1600
X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)
# Using embedding from Keras
model = Sequential()
model.add(Embedding(top_words, embedding_vecor_length,
input_length=max_review_length, name="embeddinglayer", weights=[embeddings_matrix], trainable=True))
# Convolutional model (3x conv, flatten, 2x dense)
model.add(Convolution1D(64, 3, padding='same'))
model.add(Convolution1D(32, 3, padding='same'))
model.add(Convolution1D(16, 3, padding='same'))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(180, activation='sigmoid'))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
# Log to tensorboard
tensorBoardCallback = TensorBoard(log_dir='./logs', write_graph=True)
model.compile(loss='binary_crossentropy',
optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=3, callbacks=[
tensorBoardCallback], batch_size=64)
# Evaluation on the test set
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))
The code bellow build two tensorboard graphs for the same model, while using Keras API build nice simple graph, using tf.summary.trace_export() add for each variable define in the graph a node in the external scope with the suffix "readvariableop_resource", which make the graph be really messy as the number of the parameters increase.
(In the example below we have 2 dense layer each one have 2 variable (kernel and bias) total 4 variables (4 nodes))
from datetime import datetime
import tensorflow as tf
from tensorflow import keras
# Define the model.
model = keras.models.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(32, activation='relu'),
keras.layers.Dropout(0.2),
keras.layers.Dense(10, activation='softmax')
])
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
(train_images, train_labels), _ = keras.datasets.mnist.load_data()
train_images = train_images / 255.0
# Define the Keras TensorBoard callback.
logdir="logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
# Train the model.
model.fit(
train_images,
train_labels,
batch_size=64,
epochs=1,
callbacks=[tensorboard_callback])
#tf.function
def traceme(x):
return model(x)
logdir="logs/fit1/" + datetime.now().strftime("%Y%m%d-%H%M%S")
writer = tf.summary.create_file_writer(logdir)
tf.summary.trace_on(graph=True)
# Forward pass
traceme(tf.zeros((1, 28, 28, 1)))
with writer.as_default():
tf.summary.trace_export(name="model_trace", step=0)
I made an easy KI learning with tensorflow 2 with this code and everything works fine.
# Install TensorFlow
import tensorflow as tf
print(tf.__version__)
# Import matplotlib library
import matplotlib.pyplot as plt
#Import numpy
import numpy as np
#Dataset
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
print("Evaluation");
model.evaluate(x_test, y_test)
plt.imshow(x_train[6], cmap="gray") # Import the image
plt.show() # Plot the image
predictions = model.predict([x_train]) # Make prediction
print("Vorhersage: ", np.argmax(predictions[6])) # Print out the number
print("Correct is: ", y_train[6])
My problem is how to add the detecting layers like Conv2d and MaxPooling2D. Where do I have to add this layers and does this influence my plotting and my predictions?
Before passing input to Convolution2d and maxpool2d, input must have 4 dimensions.
x_train and x_test have shape
[BatchSize, 28, 28] but it should be [BatchSize, 28, 28, 1].
So we are going to add channel dimension at last using np.expand_dims()
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), padding="same", input_shape=(None, 28, 28, 1)),
tf.keras.layers.Activation("relu"),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
Yes, It is going to influence your ploting and predictions.
Convolution layer uses less numbers of weights as compare to dense layer and then Maxpool will take features with only max values to make predictions. Which will reduce your feature because of this may be your accuracy will decrease.
Although, When we have images with large size like 500*500 then we have to apply Convolution and maxpool layers to reduce the features by selecting only important features.
If we apply flatten and dense function on input of 500*500 then program have to initialize large number of weights and you can get Out Of Memory error.
I just started with deep learning and i want to get the input/output of each layer in real-time. I am using google colab with tensorflow 2 and python 3. I tried to get the layers like this but for some reason that i don't understand is not working. Any help will be appreciated.
# Here are imports
from __future__ import absolute_import, division, print_function, unicode_literals
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 2.x
except Exception:
pass
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
from tensorflow.keras import backend as K
# I am using CIFAR10 dataset
(train_images, train_labels), (test_images, test_labels) =
datasets.cifar10.load_data()
Normalize pixel values to be between 0 and 1
train_images, test_images = train_images / 255.0, test_images / 255.0
# Here is the model
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
# Compilation of the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(train_images, train_labels, epochs=10,
validation_data=(test_images, test_labels))
# Based on
https://stackoverflow.com/questions/41711190/keras-how-to-get-the-output-of-each-layer
# I tried this
tf.compat.v1.disable_eager_execution()
inp = model.input # input placeholder
outputs = [layer.output for layer in model.layers] # all layer outputs
functors = [K.function([inp, K.learning_phase()], [out]) for out in outputs] # evaluation functions
Testing
test = np.random.random(input_shape)[np.newaxis,...]
layer_outs = [func([test, 1.]) for func in functors]
print(layer_outs)
#The error appear at line
functors = [K.function([inp, K.learning_phase()], [out]) for out in outputs]
#I got this error message
Tensor Tensor("conv2d/Identity:0", shape=(None, 30, 30, 32), dtype=float32) is not an element of this graph.
This error basically tells you that you want to change the graph after compiling it. When you call compile, TF will statically define all operations. You have to move the code snippet where you define functors above the compile method. Just swap the last lines with these ones:
tf.compat.v1.disable_eager_execution()
inp = model.input # input placeholder
outputs = [layer.output for layer in model.layers] # all layer outputs
functors = [K.function([inp, K.learning_phase()], [out]) for out in outputs] # evaluation functions
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(train_images, train_labels, epochs=1,
validation_data=(test_images, test_labels))
#Testing
input_shape = [1] + list(model.input_shape[1:])
test = np.random.random(input_shape)
layer_outs = [func([test, 1.]) for func in functors]
print(layer_outs)
I am using keras from tensorflow backend to detect potholes and garbage in my trained model. The output generated for detecting potholes or garbage works fine but when i give a random image of a car or bike or cat or human or building it identifies everything as a garbage class.what should i do now? what should be the optimal output for such cases. I am providig my code here.
#train
import numpy as np
np.random.seed(123) # for reproducibility
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from dataset_pothole import pothole
from keras.models import model_from_json
# 4. Load pre-shuffled MNIST data into train and test sets
(X_train, y_train), (X_test, y_test) = pothole.load_data()
# 5. Preprocess input data
X_train = X_train.reshape(X_train.shape[0], 50, 50, 3)
X_test = X_test.reshape(X_test.shape[0], 50, 50, 3)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
# 6. Preprocess class labels
Y_train = np_utils.to_categorical(y_train, 2)
Y_test = np_utils.to_categorical(y_test, 2)
# 7. Define model architecture
model = Sequential()
model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(50, 50, 3)))
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))
# 8. Compile model
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# 9. Fit model on training data
model.fit(X_train, Y_train,
batch_size=32, nb_epoch=20, verbose=1)
# 10. Evaluate model on test data
score = model.evaluate(X_test, Y_test, verbose=0)
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")
print('Test loss: ', score[0])
print('Test accuracy: ', score[1])
#evaluation
import numpy as np
np.random.seed(123) # for reproducibility
import keras
from keras.utils import np_utils
from keras.models import model_from_json
import os
from PIL import Image
from numpy import *
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
img = input("Please enter the test filename:")
test = array(array(Image.open(img)).flatten())
print(test.shape)
X_test = test.reshape((1, 50, 50, 3))
print(X_test.shape)
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model.h5")
#loaded_model.compile(loss='categorical_crossentropy',
# optimizer='adam',
# metrics=['accuracy'])
prediction = loaded_model.predict_classes(X_test)
print(prediction)
print(loaded_model.predict(X_test))
if prediction==[1]:
print("Pothole")
elif prediction==[0]:
print("Garbage")
else:
print("Invalid Image!")