CODEWITHSUNDEEP
tensorflowkerasdeep-learningconv-neural-network
JCV
JCV

Reputation: 515

How to match dimensions in CNN

I'm trying to build a CNN, where the goal is from 3 features to predict the label, but is giving an error of dimension. Could someone help me? updated after comments from @M.Innat

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D
from tensorflow.keras.models import Sequential, load_model
from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.optimizers import Adam
from sklearn import metrics
import tensorflow as tf
import random 

# Create data
n = 8500
l = [2, 3, 4, 5,6]
k = int(np.ceil(n/len(l)))
labels = [item for item in l for i in range(k)]
random.shuffle(labels,random.random)
labels =np.array(labels)
label_unique = np.unique(labels)


x = np.linspace(613000, 615000, num=n) + np.random.uniform(-5, 5, size=n)
y = np.linspace(7763800, 7765800, num=n) + np.random.uniform(-5, 5, size=n)
z = np.linspace(1230, 1260, num=n) + np.random.uniform(-5, 5, size=n)

X = np.column_stack((x,y,z))
Y = labels
# Split the dataset into training and testing.
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1234)
seq_len=len(X_train)
n_features=len(X_train[0])
droprate=0.1
exit_un=len(label_unique)
seq_len=len(X_train)
n_features=len(X_train[0])
droprate=0.1
exit_un=len(label_unique)
print('n_features: {} \n seq_len: {} \n exit_un: {}'.format(n_features,seq_len,exit_un))
X_train = X_train[..., None][None, ...] # add channel axis+batch aix
Y_train = pd.get_dummies(Y_train) # transform to one-hot encoded 

drop_prob = 0.5
my_model = Sequential()
my_model.add(Conv2D(input_shape=(seq_len,n_features,1),filters=32,kernel_size=(3,3),padding='same',activation="relu"))        # 1 channel of grayscale.
my_model.add(MaxPooling2D(pool_size=(2,1)))
my_model.add(Conv2D(filters=64,kernel_size=(5,5), padding='same',activation="relu"))
my_model.add(MaxPooling2D(pool_size=(2,1)))
my_model.add(Flatten())
my_model.add(Dense(units = 1024, activation="relu"))
my_model.add(Dropout(rate=drop_prob))
my_model.add(Dense(units = exit_un, activation="softmax"))

n_epochs = 100
batch_size = 10 
learn_rate = 0.005

# Define the optimizer and then compile.
my_optimizer=Adam(lr=learn_rate)
my_model.compile(loss = "categorical_crossentropy", optimizer = my_optimizer, metrics=['categorical_crossentropy','accuracy'])

my_summary = my_model.fit(X_train, Y_train, epochs=n_epochs, batch_size = batch_size, verbose = 1)

The error I have is:

ValueError: Data cardinality is ambiguous: x sizes: 1 y sizes: 5950 Make sure all arrays contain the same number of samples.

Upvotes: 0

Views: 203

Answers (1)

Innat
Innat

Reputation: 17239

You're passing the input sample without the channel axis and also the batch axis. Also, according to your loss function, you should transform your integer label to one-hot encoded.

exit_un=len(label_unique)
drop_prob = 0.5

X_train = X_train[..., None][None, ...] # add channel axis+batch aix
X_train = np.repeat(X_train, repeats=100, axis=0) # batch-ing
Y_train = np.repeat(Y_train, repeats=100, axis=0) # batch-ing
Y_train = pd.get_dummies(Y_train) # transform to one-hot encoded 
print(X_train.shape, Y_train.shape)

my_model = Sequential()
...

update

Based on the discussion, it seems like you need the conv1d operation in the modeling time and need to reshape your sample as mentioned in the comment. Here is the colab, it should work now.

Upvotes: 1

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