I have an array data which has 5000 row(images) and 58 columns(features). And the array is CSV format. How can i classify with CNN?
I was trying this following code, but i got error. what is wrong?
import keras
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution1D, MaxPooling2D
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
data = pd.read_csv('/home/mahfuz/Documents/AllFeatures.csv')
data.head()
data.iloc[3,1:].values
df_x = data.iloc[:,1:].values
y = data.iloc[:,1].values
df_x = np.array(df_x)
df_y = np.array(y)
df_y
df_y.shape
df_x.shape
x_train, x_test, y_train, y_test = train_test_split(df_x,df_y,test_size=0.2,random_state=4)
model = Sequential()
model.add(Convolution1D(32, (3, 3), input_shape= ( 32, 32, 3)))
model.add(MaxPooling1D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(100))
model.add(Dropout(0.5))
model.add(Dense(10))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',optimizer = 'adadelta', metrices = ['accuracy'])
model.fit(x_train,y_train,validation_data=(x_test,y_test))
model.evaluate(x_test,y_test)
Can I ask you what is the error?
First, Your data shape in the model is (32,32,3) on Conv1D, check it.
Your images shape is (32, 32, 3) or you have data with shape (5000, 58) ?
Related
I'm trying to fit a simple CNN on the Fashion MNIST dataset. However, I run into a ValueError. I've browsed the web on this issues and found a lot about changning loss-function and/or activation function on the output layer. But I want to have my label-variable as a one-hot-encoded and have therefore choosed 'sigmoid' and 'categorical_crossentropy'.
Help that could push me in the right direction would be much appreciated.
import tensorflow as tf
import keras
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import numpy as np
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D
(X_train_val, y_train_val), (X_test, y_test) = fashion_mnist.load_data()
X_train, X_val, y_train, y_val = train_test_split(X_train_val,
y_train_val,
test_size=0.15,
random_state=42)
X_train = X_train/255
X_val = X_val/255
X_test = X_test/255
y_train_ohe = keras.utils.np_utils.to_categorical(y_train, 10)
y_val_ohe = keras.utils.np_utils.to_categorical(y_val, 10)
y_test_ohe = keras.utils.np_utils.to_categorical(y_test, 10)
model = keras.models.Sequential([
keras.layers.Conv2D(64,7, activation='relu', input_shape=(28, 28, 1)),
keras.layers.MaxPooling2D((2)),
keras.layers.Flatten(),
keras.layers.Dense(100, activation='relu'),
keras.layers.Dense(10, activation='sigmoid')
])
model.compile(loss="categorical_crossentropy", optimizer="nadam", metrics=["accuracy"])
model.summary()
history = model.fit(X_train, y_train_ohe, epochs=2, validation_data=(X_val, y_val))
In the code below, I am getting the error:
raise ValueError('This model has not yet been built. ' ValueError: This model has not yet been built. Build the model first by calling ``build()`` or calling ``fit()`` with some data, or specify an ``input_shape`` argument in the first layer(s) for automatic build.
I am trying to use a spectrogram instead of the Mel feature extraction for this CNN. I think the error may be in the extraction process for the extract_features step.
Any help will be greatly appreciated in figuring out the source of this error.
Here are some sizes of arrays generated before the error:
x_test = {ndarray:(353,128)}
x_train = {ndarray:(1408,128)}
y = {ndarray:(1761,)}
y_test = {ndarray:(353,10)}
y_ttrain = {ndarray:(1408,10)}
CODE
# Sample rate conversion
import librosa
import librosa.display
from scipy.io import wavfile as wav
import numpy as np
import pandas as pd
import os
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
max_pad_len = 174
n_mels = 128
def extract_features(file_name):
try:
audio, sample_rate = librosa.core.load(file_name, res_type='kaiser_fast')
mely = librosa.feature.melspectrogram(y=audio, sr=sample_rate, n_mels=n_mels)
# pad_width = max_pad_len - mely.shape[1]
# mely = np.pad(mely, pad_width=((0, 0), (0, pad_width)), mode='constant')
melyscaled = np.mean(mely.T, axis=0)
except Exception as e:
print("Error encountered while parsing file: ", file_name)
return None
return melyscaled
features = []
# Iterate through each sound file and extract the features
from time import sleep
#from tqdm import tqdm
from alive_progress import alive_bar
items = len(metadata.index)
with alive_bar(items) as bar:
for index, row in metadata.iterrows():
bar()
file_name = os.path.join(os.path.abspath(fulldatasetpath), 'fold' + str(row["fold"]) + '/',
str(row["slice_file_name"]))
class_label = row["class_name"]
data = extract_features(file_name)
features.append([data, class_label])
# Convert into a Panda dataframe
featuresdf = pd.DataFrame(features, columns=['feature', 'class_label'])
print('Finished feature extraction from ', len(featuresdf), ' files')
from sklearn.preprocessing import LabelEncoder
from keras.utils import to_categorical
# Convert features and corresponding classification labels into numpy arrays
X = np.array(featuresdf.feature.tolist())
y = np.array(featuresdf.class_label.tolist())
# Encode the classification labels
le = LabelEncoder()
yy = to_categorical(le.fit_transform(y))
# split the dataset
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, yy, test_size=0.2, random_state = 42)
### Convolutional Neural Network (CNN) model architecture
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.optimizers import Adam
from keras.utils import np_utils
from sklearn import metrics
num_rows = 40
num_columns = 174
num_channels = 1
#
#x_train = x_train.reshape(x_train.shape[0], num_rows, num_columns, num_channels)
#x_test = x_test.reshape(x_test.shape[0], num_rows, num_columns, num_channels)
num_labels = yy.shape[1]
filter_size = 2
# Construct model
model = Sequential()
#model.add(Conv2D(filters=16, kernel_size=2, input_shape=(num_rows, num_columns, num_channels), activation='relu'))
model.add(Conv2D(filters=16, kernel_size=2, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv2D(filters=32, kernel_size=2, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv2D(filters=64, kernel_size=2, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv2D(filters=128, kernel_size=2, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(GlobalAveragePooling2D())
model.add(Dense(num_labels, activation='softmax'))
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
# Display model architecture summary
model.summary()
You have to build your model with your input shape before calling model.summary():
model.build((128,))
Trying to train a convolutional neural network using tensorflow and keras on python 3 in an anaconda environment. I think it's to do with how the dataset was saved in pickle previously but not completely sure
This is the error I keep getting:
ValueError: Failed to find data adapter that can handle input: <class 'numpy.ndarray'>, (<class 'list'> containing values of types {"<class 'int'>"})
This is my code:
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
import pickle
pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)
pickle_in = open("Y.pickle","rb")
y = pickle.load(pickle_in)
X = X/255.0
model = Sequential()
model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X, y, batch_size=32, epochs=3, validation_split=0.3)
This code and question has been asked on StackExchange before:
https://datascience.stackexchange.com/questions/60035/keras-error-failed-to-find-data-adapter-that-can-handle-input-while-trying-to
edit:
To fix your problem, you can try to change how you import the data and make sure it's a valid numpy array.
At the top, import numpy as np and then change where you load the data and define X and y to: X = np.asarray(pickle.load(open("X.pickle", "rb"))) and then: y = np.asarray(pickle.load(open("y.pickle", "rb")))
I am also facing the same issue however it works after importing NumPy.
import numpy as np
pickle_in = open("X.pickle","rb")
X = np.asarray(pickle.load(pickle_in))
pickle_in = open("y.pickle","rb")
y = np.asarray(pickle.load(pickle_in))
I have to perform a simple FSGM attack to a convolutional neural network. The code for the CNN works correctly, and the model is saved without a problem, but when i try to perform the attack an error is shown.
HERE'S THE CODE FOR THE CNN
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.utils import to_categorical
import json
import tensorflow as tf
#Using TensorFlow backend.
#download mnist data and split into train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
#plot the first image in the dataset
plt.imshow(X_train[0])
#check image shape
X_train[0].shape
#reshape data to fit model
X_train = X_train.reshape(60000,28,28,1)
X_test = X_test.reshape(10000,28,28,1)
#one-hot encode target column
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
y_train[0]
#create model
model = Sequential()
#add model layers
model.add(Conv2D(32, kernel_size=(5,5), activation='relu', input_shape= (28,28,1)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64, kernel_size=(5,5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
#compile model using accuracy as a measure of model performance
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics= ['accuracy'])
#train model
model.fit(X_train, y_train,validation_data=(X_test, y_test), epochs=5)
json.dump({'model':model.to_json()},open("model.json", "w"))
model.save_weights("model_weights.h5")
THEN I TRY TO PERFORM THE ATTACK WITH THE FOLLOWING CODE:
import json
import foolbox
import keras
import numpy as np
from keras import backend
from keras.models import load_model
from keras.datasets import mnist
from keras.utils import np_utils
from foolbox.attacks import FGSM
from foolbox.criteria import Misclassification
from foolbox.distances import MeanSquaredDistance
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
import numpy as np
import tensorflow as tf
from keras.models import model_from_json
import os
############## Loading the model and preprocessing #####################
backend.set_learning_phase(False)
model = tf.keras.models.model_from_json(json.load(open("model.json"))["model"],custom_objects={})
model.load_weights("model_weights.h5")
fmodel = foolbox.models.KerasModel(model, bounds=(0,1))
_,(images, labels) = mnist.load_data()
images = images.reshape(10000,28,28)
images= images.astype('float32')
images /= 255
######################### Attacking the model ##########################
attack=foolbox.attacks.FGSM(fmodel, criterion=Misclassification())
adversarial=attack(images[12],labels[12]) # for single image
adversarial_all=attack(images,labels) # for all the images
adversarial =adversarial.reshape(1,28,28,1) #reshaping it for model prediction
model_predictions = model.predict(adversarial)
print(model_predictions)
########################## Visualization ################################
images=images.reshape(10000,28,28)
adversarial =adversarial.reshape(28,28)
plt.figure()
plt.subplot(1,3,1)
plt.title('Original')
plt.imshow(images[12])
plt.axis('off')
plt.subplot(1, 3, 2)
plt.title('Adversarial')
plt.imshow(adversarial)
plt.axis('off')
plt.subplot(1, 3, 3)
plt.title('Difference')
difference = adversarial - images[124]
plt.imshow(difference / abs(difference).max() * 0.2 + 0.5)
plt.axis('off')
plt.show()
this error is shown when the adversarial examples are generated:
c_api.TF_GetCode(self.status.status))
InvalidArgumentError: Matrix size-incompatible: In[0]: [1,639232], In[1]: [1024,10]
[[{{node dense_4_5/MatMul}}]]
[[{{node dense_4_5/BiasAdd}}]]
What could it be?
here is my solution.
First of all modify the model code as follows
import tensorflow as tf
import json
# download mnist data and split into train and test sets
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
# reshape data to fit model
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
X_train, X_test = X_train/255, X_test/255
# one-hot encode target column
y_train = tf.keras.utils.to_categorical(y_train)
y_test = tf.keras.utils.to_categorical(y_test)
# create model
model = tf.keras.models.Sequential()
# add model layers
model.add(tf.keras.layers.Conv2D(32, kernel_size=(5, 5),
activation='relu', input_shape=(28, 28, 1)))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(tf.keras.layers.Conv2D(64, kernel_size=(5, 5), activation='relu'))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(10, activation='softmax'))
# compile model using accuracy as a measure of model performance
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
# train model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5)
json.dump({'model': model.to_json()}, open("model.json", "w"))
model.save_weights("model_weights.h5")
You just forgot to divide each pixel by the maximum value of RGB (255)
As for the attacker code
import json
import foolbox
from foolbox.attacks import FGSM
from foolbox.criteria import Misclassification
import numpy as np
import tensorflow as tf
############## Loading the model and preprocessing #####################
tf.enable_eager_execution()
tf.keras.backend.set_learning_phase(False)
model = tf.keras.models.model_from_json(
json.load(open("model.json"))["model"], custom_objects={})
model.load_weights("model_weights.h5")
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
_, (images, labels) = tf.keras.datasets.mnist.load_data()
images = images.reshape(images.shape[0], 28, 28, 1)
images = images/255
images = images.astype(np.float32)
fmodel = foolbox.models.TensorFlowEagerModel(model, bounds=(0, 1))
######################### Attacking the model ##########################
attack = foolbox.attacks.FGSM(fmodel, criterion=Misclassification())
adversarial = np.array([attack(images[0], label=labels[0])])
model_predictions = model.predict(adversarial)
print('real label: {}, label prediction; {}'.format(
labels[0], np.argmax(model_predictions)))
I used TensorFlowEagerModel instead of KerasModel for simplicty. The error you were encountering was due to the fact that model.predict expects a 4d matrix while you were passing a 3d matrix, so I just wrapped up the attack to the image example into a numpy array to make it 4d.
Hope it helps
I have a model and although I have a flatten layer it keeps giving me the error:
ValueError: Error when checking target: expected dense_53 to have 2 dimensions, but got array with shape (1, 1, 28, 28)
Additionaly, if I remove the dense and flatten layers it gives me the same error for the maxPooling and then for the convolutional. Here is my entire code:
import os
import numpy as np
from keras.preprocessing import image
from keras import backend as K
from keras.callbacks import EarlyStopping
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D
from keras.optimizers import Adam
from keras.layers.pooling import MaxPooling2D
from keras.utils import to_categorical
PATH = os.getcwd()
train_path = PATH+'/Downloads/KerasStuff/train_data/rice/'
train_batch = os.listdir(train_path)
x_train = np.empty((0,32,32,3))
# if data are in form of images
for sample in train_batch:
img_path = train_path+sample
if img_path == '/Users/23athreyad/Downloads/KerasStuff/train_data/rice/.DS_Store':
print("INVALID")
else:
x = image.load_img(img_path)
np.append(x_train,x)
test_path = PATH+'/Downloads/KerasStuff/test_data/rice/'
test_batch = os.listdir(test_path)
x_test = []
for sample in test_batch:
img_path = test_path+sample
x = image.load_img(img_path)
# preprocessing if required
x_test.append(x)
# Create the model
K.set_image_dim_ordering('tf')
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(MaxPooling2D(pool_size = (2,2),data_format="channels_last"))
print(model.output_shape)
model.add(Dropout(0.25))
model.add(Flatten())
print(model.output_shape)
model.add(Dense(7200,activation = 'relu'))
# Compile the model
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0.0001, decay=1e-6),
metrics=['accuracy'])
model.fit(x_train,y_train,batch_size = 10, epochs = 20,callbacks=[EarlyStopping(min_delta=0.001, patience=3)])
Can someone help me with this? I am new to Keras and have looked this up but not found any solutions.