I tried to train the mnist dataset with tensorflow but i got error on Data cardinality. I tried to print out the shape of trainX, trainY, testX, testY and everything was ok but it still gave me that error. I saw this kind of question was asked before but I just don't understand it.
shape of trainX, trainY, testX, testY
(60000, 28, 28)
(60000,)
(10000, 28, 28)
(10000,)
my code
from tensorflow.keras import Sequential
from tensorflow.keras.layers import (
Conv2D, Activation, MaxPooling2D, Flatten, Dense
)
from tensorflow.keras import backend as K
from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import classification_report
from tensorflow.keras.datasets import mnist
from tensorflow.keras import backend as K
import numpy as np
class LeNet:
@staticmethod
def build(width, height, depth, classes):
model = Sequential()
inputShape = (width, height, depth)
if K.image_data_format() == "channels_first":
inputShape = (depth, width, height)
# first set of CONV
model.add(Conv2D(20, (5, 5), strides=(1, 1),
padding="same", input_shape=inputShape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# second set of CONV
model.add(Conv2D(50, (5, 5), strides=(1, 1), padding="same"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Fully connect layer
model.add(Flatten())
model.add(Dense(500))
model.add(Activation('relu'))
# softmax classifier
model.add(Dense(classes))
model.add(Activation('softmax'))
return model
# load mnist dataset
print("[INFO] loading mnist dataset ...")
(trainX, trainY), (testX, testY) = mnist.load_data()
print(trainX.shape)
print(trainY.shape)
print(testX.shape)
print(testY.shape)
# scale to range [0,1]
trainX = testX.astype("float") / 255.0
testX = testX.astype("float") / 255.0
label = LabelBinarizer()
trainY = label.fit_transform(trainY)
testY = label.transform(testY)
# initialize the label name for mnist dataset
labelNames = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
# initialize the optimizer and model
print("[INFO] compiling model ...")
model = LeNet.build(width=28, height=28, depth=1, classes=10)
model.compile(optimizer='adam', loss="categorical_crossentropy",
metrics=["accuracy"])
# train the network
print("[INFO] training the network ...")
H = model.fit(trainX, trainY, validation_data=(testX, testY),
batch_size=128, epochs=20, verbose=1)
# save the model
print("[INFO] saving the model ...")
model.save("leNet_MNIST.h5")
# evaluating the network
print("[INFO] evaluating the network ...")
predictions = model.predict(testX, batch_size=128)
print(classification_report(testY.argmax(axis=1), predictions.argmax(axis=1),
target_names=labelNames))
error I got
ValueError Traceback (most recent call last)
<ipython-input-5-20120316873b> in <module>()
71 print("[INFO] training the network ...")
72 H = model.fit(trainX, trainY, validation_data=(testX, testY),
---> 73 batch_size=128, epochs=20, verbose=1)
74
75 # save the model
3 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)
280 label, ", ".join(str(i.shape[0]) for i in nest.flatten(data)))
281 msg += "Please provide data which shares the same first dimension."
--> 282 raise ValueError(msg)
283 num_samples = num_samples.pop()
284
ValueError: Data cardinality is ambiguous:
x sizes: 10000
y sizes: 60000
Please provide data which shares the same first dimension.
There is a little mistake in your code. On the line where you are scaling your data,
# scale to range [0,1]
trainX = testX.astype("float") / 255.0
testX = testX.astype("float") / 255.0
you accidentally used testX for both your trainX and testX, resulting in trainX.shape to be (10000, 28, 28). Just change it to be
trainX = trainX.astype("float") / 255.0
and it should work.