I am trying to convert a trained model (code given below) to a theano function. But I am getting the following error: AttributeError: 'Dense' object has no attribute 'output'.
The code for my model:
model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode='same',
input_shape=(img_channels, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
# let's train the model using SGD + momentum (how original).
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
The code I am using to convert the Keras model to a theano function by following this tutorial:
from keras import backend as K
get_last_layer_output = K.function([model.layers[0].input],
[model.layers[-1].output])
y=f(patches)
Can anyone please tell me what to do?
Try model.layers[-1].get_output(train=False). The original Keras tutorial may be outdated.
Related
I am working in python and tensor flow but I miss 'units' argument and I do not know how to solve it, It looks like your post is mostly code; please add some more details.It looks like your post is mostly code; please add some more details.
here the code
def createModel():
model = Sequential()
# first set of CONV => RELU => MAX POOL layers
model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=inputShape))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(output_dim=NUM_CLASSES, activation='softmax'))
# returns our fully constructed deep learning + Keras image classifier
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
# use binary_crossentropy if there are two classes
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
return model
print("Reshaping trainX at..."+ str(datetime.now()))
#print(trainX.sample())
print(type(trainX)) # <class 'pandas.core.series.Series'>
print(trainX.shape) # (750,)
from numpy import zeros
Xtrain = np.zeros([trainX.shape[0],HEIGHT, WIDTH, DEPTH])
for i in range(trainX.shape[0]): # 0 to traindf Size -1
Xtrain[i] = trainX[i]
print(Xtrain.shape) # (750,128,128,3)
print("Reshaped trainX at..."+ str(datetime.now()))
print("Reshaping valX at..."+ str(datetime.now()))
print(type(valX)) # <class 'pandas.core.series.Series'>
print(valX.shape) # (250,)
from numpy import zeros
Xval = np.zeros([valX.shape[0],HEIGHT, WIDTH, DEPTH])
for i in range(valX.shape[0]): # 0 to traindf Size -1
Xval[i] = valX[i]
print(Xval.shape) # (250,128,128,3)
print("Reshaped valX at..."+ str(datetime.now()))
# initialize the model
print("compiling model...")
sys.stdout.flush()
model = createModel()
# print the summary of model
from keras.utils import print_summary
print_summary(model, line_length=None, positions=None, print_fn=None)
# add some visualization
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
SVG(model_to_dot(model).create(prog='dot', format='svg'))
Try changing this line:
model.add(Dense(output_dim=NUM_CLASSES, activation='softmax'))
to
model.add(Dense(NUM_CLASSES, activation='softmax'))
I'm not experience in keras but I could not find a parameter called output_dim on the documentation page for Dense. I think you meant to provide units but labelled it as output_dim
The Keras Dense layer documentation is as follows:
keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
Using the following :
classifier.add(Dense(6, activation='relu', kernel_initializer='glorot_uniform',input_dim=11))
Will work as here the units means the output_dim saying that we need 6 neurons in the hidden layer. The weights are initialized with the uniform function and the input layer has 11 independent variables of the dataset (input_dim) to feed the above-hidden layer.
I think it's a version issue. In updated version of keras for Dense there is no "output_dim" argument.
You can see this documentation link for Dense arguments.
https://keras.io/api/layers/core_layers/dense/
tf.keras.layers.Dense(
units,
activation=None,
use_bias=True,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs
)
So the first argument is "units", Which is mandatory.
instead of this line:
model.add(Dense(output_dim=NUM_CLASSES, activation='softmax'))
use this:
model.add(Dense(units=NUM_CLASSES, activation='softmax'))
or
model.add(Dense(NUM_CLASSES, activation='softmax'))
I have built a simple CNN using tensorflow-gpu 2.4.0 for cifar10
# Set constent
num_classes = len(label_names) # The number of classes
input_shape = (32, 32, 3) # The input shape 32 * 32 pixels, 3 RGB brightness for each pixel
# Construct a sequential model
model = Sequential()
# Starting add layers to the model
# First VGG blockes
model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=input_shape))
model.add(BatchNormalization()) # BatchNormalization after convolutional layer
model.add(Conv2D(16, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization()) # BatchNormalization after convolutional layer
model.add(MaxPooling2D((2, 2))) # MaxPooling2D to keep important features as well as shirnk info size
model.add(Dropout(0.1)) # Drop some portion of nodes to increase generality of model
# Second VGG blockes
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
# Third VGG blockes
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.3))
# Fourth VGG blockes
model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.4))
# Flat and dense layer
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
# Final softmax layer
model.add(Dense(num_classes, activation='softmax'))
# The optimizer gives the learning rate lr and some other parameters.
opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
# Model complie
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
And while I am training this model:
#Train model
epochs = 100
batch_size = 128
# create data generator
datagen = ImageDataGenerator(horizontal_flip=True,
width_shift_range=5,
height_shift_range=5,
rotation_range=20)
history = model.fit(datagen.flow(x_training, y_training, batch_size=batch_size),
epochs=epochs,
validation_data=(x_validate, y_validate),
verbose=1,
shuffle=True)
The RAM of my computer keeps grow until the training process crashes. Adding python garbage collector for at end of each epoch did not work. Disabling eager detection did not work.
I using
tensorflow-gpu 2.4.0
CUDA 11.0
cuDNN 8.0
i'm trying to make a simple classification model for the cifar-10 dataset. The model fails when it gets to Maxpooling fuction. It says that it has the incorrect Syntax but for the life of me i cannot figure out whats wrong.
Is it the version of keras i'm using? when i add maxpooling to the model with a size of 2, 2 it don't work and in the documentation, i am doing the exact same thing which makes me think its a version problem.
Sorry if the problem is obvious
model = Sequential()
model.add(Conv2D(32, (3,3), padding = 'same', input_shape=(32,32,3)))
model.add(Activation('relu')
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu')
model.add(Dropout(0.5))
model.add(Dense(10))
model.add(Activation('softmax'))
model.summary()
Max pooling does not have any issue.your issue is you are missing some brackets in the previous line. find below the corrected code
model = Sequential()
model.add(Conv2D(32, (3,3), padding = 'same', input_shape=(32,32,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(10))
model.add(Activation('softmax'))
model.summary()
Hope this helps.
I am trying to make an emotion classifier using face expressions with FER2013 dataset. It contains of 35887 samples with 2304 features each and an integer label 0-6 for emotions. When I was using Conv1D with shape (2304,1) then it achieved training accuracy of ~86% but wasn't doing well on any unseen test image. So I thought of reshaping it to (48,48,1) for each sample and using Conv2D on it. But now it just gets stuck on 0.2505 while training after the 2nd epoch and never increases. What's happening?
import pandas as pd
import numpy as np
from PIL import Image
import matplotlib.image as mpimg
from skimage import transform
import random
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
emotion = {0 : 'Angry', 1 : 'Disgust',2 : 'Fear',3 : 'Happy',
4 : 'Sad',5 : 'Surprise',6 : 'Neutral'}
df=pd.read_csv('fer.csv')
faces=df.values[:,1]
faces=faces.tolist()
emos=df.values[:,0]
for i in range(len(faces)):
faces[i]=[int(x) for x in faces[i].split()]
emos[i]=int(emos[i])
faces=np.array(faces)
faces=transform.resize(faces, (35887,48,48))
faces=np.expand_dims(faces, axis=3)
model = Sequential()
model.add(Conv2D(48, (3,3), padding='same', input_shape=(48,48,1), activation='relu'))
model.add(Conv2D(48, (3,3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(96, (3,3), padding='same', activation='relu'))
model.add(Conv2D(96, (3,3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(192, (3,3), padding='same', activation='relu'))
model.add(Conv2D(192, (3,3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(384, (3,3), padding='same', activation='relu'))
model.add(Conv2D(384, (3,3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(384, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(192, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(96, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(faces,emos,epochs=100,batch_size=48)
model.save_weights('model.h5')
Model Accuracy Curves
Model Loss Curves
Normalizing the output batch after each layer fixes the issue.
Just add
model.add(BatchNormalization())
after every layer.
EDIT :
Thought that I should add more information here.
So, this was the final model I ended up making.
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(200,200,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(256, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(len(classes), activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='nadam',
metrics=['accuracy'])
And these were the results I got with it.
I increased the nodes and changed the optimizer too but it was the batch normalisation which gave a dramatic increase in accuracy. Using more nodes and nadam optimizer further helped a bit.
I have just build my first model using Phyton, Keras and Tensorflow.
My model looks like this:
model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(img_width, img_height,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
Now i'm trying to compile it,
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
but i got this error:
ValueError: Only call sigmoid_cross_entropy_with_logits with named
arguments (labels=..., logits=..., ...)
I can't seem to find what the problem is. Please let me know what I'm doing wrong..