I'm trying to extract features from a pretrained model and use on my own model. I can successfully instantiate the Inveption V3 Model and save the outputs tu use as inputs for my model, but as i try to use it i get error. I tried to delete the Flatten layer but looks like the problem isnt this. I think the problem is about the last_output but have no clue on how to solve it.
The code:
#%% Imports.
import tensorflow as tf
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras import layers, Model
from tensorflow.keras.applications.inception_v3 import InceptionV3
import os, signal
import numpy as np
#%% Instatiate an Inception V3 model
url = "https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5" # Get the weights from the pretrained model
local_weights_file = tf.keras.utils.get_file("inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5", origin = url, extract = True)
pre_trained_model = InceptionV3(input_shape=(150, 150, 3), include_top=False, weights=None) # include_top=False argument, we load a network that doesn't include
pre_trained_model.load_weights(local_weights_file) # the classification layers at the top—ideal for feature extraction.
# Make the model non-trainable, since we will only use it for feature extraction; we won't update the weights of the pretrained model during training.
for layers in pre_trained_model.layers:
layers.trainable = False
# The layer we will use for feature extraction in Inception v3 is called mixed7. It is not the bottleneck of the network, but we are using it to keep a
# sufficiently large feature map (7x7 in this case). (Using the bottleneck layer would have resulting in a 3x3 feature map, which is a bit small.)
last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape:', last_layer.output_shape)
last_output = last_layer.output
print(last_output)
# %% Stick a fully connected classifier on top of last_output
# Flatten the output layer to 1 dimension
x = layers.Flatten()(last_output)
# Add a fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dense(1024, activation='relu')(x)
# Add a dropout rate of 0.2
x = layers.Dropout(0.2)(x)
# Add a final sigmoid layer for classification
x = layers.Dense(1, activation='sigmoid')(x)
# Configure and compile the model
model = Model(pre_trained_model.input, x)
model.compile(loss='binary_crossentropy',
optimizer=RMSprop(lr=0.0001),
metrics=['acc'])
the error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
c:\Users\jpaul\Code\Google_ML_Crash_Course\02_Practica\02_Image_Classification\image_classification_part3.py in
39 # Flatten the output layer to 1 dimension
----> 40 x = layers.Flatten()(last_output)
41
42 # Add a fully connected layer with 1,024 hidden units and ReLU activation
43 x = layers.Dense(1024, activation='relu')(x)
AttributeError: 'Concatenate' object has no attribute 'Flatten'
In your for loop, you overwrote the layers identifier from the import statement of
from tensorflow.keras import layers
So when you try to create a new Flatten() layer, the identifier layers contains a Concatenate object rather than the Keras layers module you were expecting.
Change the variable name in your for loop and you should be good.
Related
Hello I have been trying to create two tensorflow models to experiment with transfer learning. I have a trainned a cnn model for lung xray images for pneumonia(2 classes) by using the kaggle chest x-ray dataset .
Here is my code
import tensorflow as tf
import numpy as np
from tensorflow import keras
import os
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
import matplotlib.pyplot as plt
gen = ImageDataGenerator(rescale=1./255)
train_data = gen.flow_from_directory("/Users/saibalaji/Downloads/chest_xray/train",target_size=(500,500),batch_size=32,class_mode='binary')
test_data = gen.flow_from_directory("/Users/saibalaji/Downloads/chest_xray/test",target_size=(500,500),batch_size=32,class_mode='binary')
model = keras.Sequential()
# Convolutional layer and maxpool layer 1
model.add(keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(500,500,3)))
model.add(keras.layers.MaxPool2D(2,2))
# Convolutional layer and maxpool layer 2
model.add(keras.layers.Conv2D(64,(3,3),activation='relu'))
model.add(keras.layers.MaxPool2D(2,2))
# Convolutional layer and maxpool layer 3
model.add(keras.layers.Conv2D(128,(3,3),activation='relu'))
model.add(keras.layers.MaxPool2D(2,2))
# Convolutional layer and maxpool layer 4
model.add(keras.layers.Conv2D(128,(3,3),activation='relu'))
model.add(keras.layers.MaxPool2D(2,2))
# This layer flattens the resulting image array to 1D array
model.add(keras.layers.Flatten())
# Hidden layer with 512 neurons and Rectified Linear Unit activation function
model.add(keras.layers.Dense(512,activation='relu'))
# Output layer with single neuron which gives 0 for Cat or 1 for Dog
#Here we use sigmoid activation function which makes our model output to lie between 0 and 1
model.add(keras.layers.Dense(1,activation='sigmoid'))
hist = model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
model.fit_generator(train_data,
steps_per_epoch = 163,
epochs = 4,
validation_data = test_data
)
I have saved the model in .h5 format.
Then I created a new notebook loaded data from kaggle for alzheimer's disease and loaded my saved pneumonia model. Copied its layer to a new model except last layer then Freezed all the layers in the new model as non trainable. Then added a output dense layer with 4 neurons for 4 classes. Then trainned only the last layer for 5 epochs. But the problem is val accuaracy remains at 35% constant. How can I improve it.
Here is my code for alzeihmers model
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
gen = ImageDataGenerator(rescale=1./255)
traindata = datagen.flow_from_directory('/Users/saibalaji/Documents/TensorFlowProjects/ad/train',target_size=(500,500),batch_size=32)
testdata = datagen.flow_from_directory('/Users/saibalaji/Documents/TensorFlowProjects/ad/test',target_size=(500,500),batch_size=32)
model = keras.models.load_model('pn.h5')
nmodel = keras.models.Sequential()
#add all layers except last one
for layer in model.layers[0:-1]:
nmodel.add(layer)
for layer in nmodel.layers:
layer.trainable = False
nmodel.add(keras.layers.Dense(units=4,name='dense_last'))
hist = nmodel.compile(optimizer = tf.keras.optimizers.Adam(learning_rate=0.002),loss = 'categorical_crossentropy', metrics = ['accuracy'])
nmodel.fit(x=traindata,validation_data=testdata,epochs=5,steps_per_epoch=160)
Here is my prediction code.
class_labels = []
for class_label,class_mode in traindata.class_indices.items():
print(class_label)
class_labels.append(class_label)
def predictimage(filepath):
test_image = image.load_img(path=filepath,target_size=(500,500))
image_array = image.img_to_array(test_image)
image_array = image_array / 255
print(image_array.shape)
image_array_exp = np.expand_dims(image_array,axis=0)
result = nmodel.predict(image_array_exp)
print(result)
plt.imshow(test_image)
plt.xlabel(class_labels[np.argmax(result)])
I also noticed that it is predicting only two classes even though I have changed the last layer to 4 neurons, changed the loss function.
It looks like you didn't add an activation function in your last layer.
Maybe it would be helpful to use the softmax activation function.
nmodel.add(keras.layers.Dense(units=4, activation= "softmax", name='dense_last'))
I have a built a LSTM architecture using Keras. My goal is to map length 29 time series input sequences of floats to length 29 output sequences of floats. I am trying to implement a "many-to-many" approach. I followed this post for implementing such a model.
I start by reshaping each data point into an np.array of shape `(1, 29, 1). I have multiple data points and train the model on each one separately. The following code is how I build my model:
def build_model():
# define model
model = tf.keras.Sequential()
model.add(tf.keras.layers.LSTM(29, return_sequences=True, input_shape=(29, 1)))
model.add(tf.keras.layers.LeakyReLU(alpha=0.3))
model.compile(optimizer='sgd', loss='mse', metrics = ['mae'])
#cast data
for point in train_dict:
train_data = train_dict[point]
train_dataset = tf.data.Dataset.from_tensor_slices((
tf.cast(train_data[0], features_type),
tf.cast(train_data[1], target_type))
).repeat() #cast into X, Y
# fit model
model.fit(train_dataset, epochs=100,steps_per_epoch = 1,verbose=0)
print(model.summary())
return model
I am confused because when I call model.predict(test_point, steps = 1, verbose = 1) the model returns 29 length 29 sequences! I don't understand why this is happening, based on my understanding from the linked post. When I try return_state=True instead of return_sequences=True then my code raises this error: ValueError: All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.
How do I solve the problem?
Your model has few flaws.
The last layer of your model is an LSTM. Assuming you're doing either classification / regression. This should be followed by a Dense layer (SoftMax/sigmoid - classification, linear - regression). But since this is a time-series problem, dense layer should be wrapped in a TimeDistributed wrapper.
It's odd to apply a LeakyReLU on top of the LSTM.
I've fixed the code with fixes for above issues. See if that helps.
from tensorflow.keras.layers import Embedding, Input, Bidirectional, LSTM, Dense, Concatenate, LeakyReLU, TimeDistributed
from tensorflow.keras.initializers import Constant
from tensorflow.keras.models import Model
from tensorflow.keras.models import Sequential
def build_model():
# define model
model = Sequential()
model.add(LSTM(29, return_sequences=True, input_shape=(29, 1)))
model.add(TimeDistributed(Dense(1)))
model.compile(optimizer='sgd', loss='mse', metrics = ['mae'])
print(model.summary())
return model
model = build_model()
I have a multi input Keras neural network where I want to calculate the gradient of the output with respect to one of the intermediate layers. The layer I chose is inside one of my sequential models that handles one of the two inputs to the model.
But when I try to calculate the gradient with respect to this layer I get [None] returned as the output. I am able to take the gradient for other layers such as the concatenate layer, but not any layers within my 2 branches. Is it possible for me to take the gradient of this layer given that it is within a sequential wrapper in my model?
Here is some code that shows what I'm trying to do.
from keras.models import Sequential, Model
from keras.layers import Dense, Input, concatenate
import keras.backend as K
# Init branch 1
branch1 = Sequential()
branch1.add(Dense(64, activation='relu', input_shape=(1000,)))
branch1.add(Dense(32, activation='relu'))
# Init branch 2
branch2 = Sequential()
branch2.add(Dense(16, activation='relu', input_shape=(500,)))
branch2.add(Dense(8, activation='relu'))
branch1_input = Input(shape=(1000,))
branch1_out = branch1(branch1_input)
branch2_input = Input(shape=(500,))
branch2_out = branch2(branch2_input)
# Combine
x = concatenate([branch1_out, branch2_out])
out = Dense(1)(x)
# Create model
model = Model(inputs=[branch1_input, branch2_input], outputs=out)
layer = model.get_layer('sequential_1').get_layer('dense_2')
grads = K.gradients(model.output, layer.output)
print(grads) # prints out [None]
I am trying to finetune inception-v3, so that it is able to make a decision between an image with signal present and signal absent. How do I edit the code so that it can train on my data? Here is the code to finetune inception-v3:
from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
# create the base pre-trained model
base_model = InceptionV3(weights='imagenet',
include_top=False)
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)
# and a logistic layer -- let's say we have 200 classes
predictions = Dense(200, activation='softmax')(x)
# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)
# first: train only the top layers (which were randomly
# initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in base_model.layers:
layer.trainable = False
# compile the model (should be done *after* setting layers
# to non-trainable)
model.compile(optimizer='rmsprop',loss='categorical_
crossentropy')
# train the model on the new data for a few epochs
model.fit_generator(...)
# at this point, the top layers are well trained and we can
# start fine-tuning
# convolutional layers from inception V3. We will freeze the
# bottom N layers
# and train the remaining top layers.
# let's visualize layer names and layer indices to see how
# many layers
# we should freeze:
for i, layer in enumerate(base_model.layers):
print(i, layer.name)
# we chose to train the top 2 inception blocks, i.e. we will
#freeze
# the first 249 layers and unfreeze the rest:
for layer in model.layers[:249]:
layer.trainable = False
for layer in model.layers[249:]:
layer.trainable = True
# we need to recompile the model for these modifications to
# take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
loss='categorical_crossentropy')
# we train our model again (this time fine-tuning the top 2
#inception blocks
# alongside the top Dense layers
model.fit_generator(...)
I would greatly appreciate any help you might give.
I want to us a pre-trained model to classify images. Every image can have multiple classes (in this case up to 21). Of course not all classes occur in every image. Therefore, I want to add a Masking layer to the model which masks all zeros (given value if a class doesn't exist).
Now I've never used a Masking layer before, but in most online examples it is added directly after the Input layer. This is, however, not possible in the example below, because the Masking layer is not compatible with all layers (in this case a Conv2D layer.
It was possible to insert the Masking layer before the fully-connected output layer. But this did not really affect the loss of the model (loss is around 20!).
Am I doing it right or is adding the Masking layer at the top of the model totally useless?
My Model
from keras.layers import Input, GlobalAveragePooling2D, Masking, Dense, Dropout
from keras.applications.inception_v3 import InceptionV3
from keras.models import Model
#Define input tensor
input_tensor = Input(shape=(512, 512, 3))
# create the base pre-trained model
base_model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=False)
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a Mask fully-connected layer and a dropout
x = Masking(0.)(x)
x = Dense(1024, activation='relu')(x)
x = Dropout(0.2)(x)
# and a logistic layer with 21 classes
predictions = Dense(21, activation='sigmoid')(x)
# create final model
model = Model(inputs=base_model.input, outputs=predictions)
# compile model
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
Example labels
# input
np.array([[1,2,0,0,0,6,7,8,0,0,0,12,13,14,15,16,17,18,19,20,21],
[0,2,0,4,5,6,7,8,9,10,11,0,0,14,15,16,17,18,19,20,21]])