When running the code below I get an error, saying there is a shape mismatch, after searching on google I found this is commonly caused by using cross_entropy as a loss function when the target is not one-hot encoded. Since mine isn't one-hot encoded I used sparse_categorical_crossentropy instead, but the error is still there.
Here is the error I recieved :
Train on 60000 samples
Epoch 1/100
32/60000 [..............................] - ETA: 1:18
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-123-4ce1d4d047d4> in <module>()
----> 1 model.fit(x_train, y_train, batch_size=32, epochs=100, verbose=1)
25 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_ops.py in sparse_softmax_cross_entropy_with_logits(_sentinel, labels, logits, name)
3391 "should equal the shape of logits except for the last "
3392 "dimension (received %s)." % (labels_static_shape,
-> 3393 logits.get_shape()))
3394 # Check if no reshapes are required.
3395 if logits.get_shape().ndims == 2:
ValueError: Shape mismatch: The shape of labels (received (32,)) should equal the shape of logits except
for the last dimension (received (5408, 10)).
This is my code:
import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import fashion_mnist
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = np.reshape(x_train, (-1,28,28,1))
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=2, input_shape=[28,28,1], activation='relu'))
model.add(tf.keras.layers.Dense(units = 10, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
model.fit(x_train, y_train, batch_size=32, epochs=100, verbose=1)
I only just realized I had forgotten to add 'flatten' layer to the model:
model.add(tf.keras.layers.Flatten())
which has resolved the issue.
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=2, input_shape= [28,28,1], activation='relu'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(units = 10, activation='softmax'))
Related
This is my code:
X_train, Y_train, X_test, Y_test = load_data(DATA_PATH)
model = keras.Sequential([
# input layer
# 1st dense layer
keras.layers.Dense(256, activation='relu', input_shape=(X_train.shape[1], X_train.shape[2], X_train.shape[3])),
# 2nd dense layer
keras.layers.Dense(128, activation='relu'),
# 3rd dense layer
keras.layers.Dense(64, activation='relu'),
# output layer
keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
model.summary()
classifier = model.fit(X_train,
Y_train,
epochs=100,
batch_size=128)
Y_train ,X_train and Y_test ,X_test are numpy arrays. X_train contains 800 and X_test 200 .png pictures of size 128X128.
Y_train contains 800 labels (80x1, 80x2, etc.) and Y_test contains testing target (20x1, 20x2, etc.).
When I try to run this program I get the following error:
ValueError: Shapes (None, 1) and (None, 128, 128, 10) are incompatible
You need to reshape your input
Here is a running code
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
X_train = tf.random.normal(shape=(800,128,128,3))
X_test = tf.random.normal(shape=(200,128,128,3))
Y_train = tf.random.normal(shape=(800,10))
Y_test = tf.random.normal(shape=(200,10))
#reshape
X_train = tf.reshape(X_train, shape=(800, 128*128*3))
model = keras.Sequential([
# input layer
# 1st dense layer
keras.layers.Dense(256, activation='relu', input_shape=(X_train.shape[0], X_train.shape[1])),
# 2nd dense layer
keras.layers.Dense(128, activation='relu'),
# 3rd dense layer
keras.layers.Dense(64, activation='relu'),
# output layer
keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
model.summary()
classifier = model.fit(X_train,
Y_train,
epochs=100,
batch_size=128)
I looked at various responses already but I dont understand why I am constantly getting (10, 5).
Why is it asking for a shape of (10,5)? Where is it even getting that number from?
I am under the impression that the shape of the input data should be ("sample_size", "steps or time_len", "channels or feat_size") => (3809, 49, 5).
I am also under the impression that the input shape for Conv1D layer should be ("steps or time_len", "channels or feat_size").
Am I misunderstanding something?
My input data looks something like this:
There is a total of 49 days, 5 data points per each day. There is a total of 5079 sample size. 75% of the data for training, 25% for validation. 10 possible prediction output answers.
x_train, x_test, y_train, y_test = train_test_split(np_train_data, np_train_target, random_state=0)
print(x_train.shape)
x_train = x_train.reshape(x_train.shape[0], round(x_train.shape[1]/5), 5)
x_test = x_test.reshape(x_test.shape[0], round(x_test.shape[1]/5), 5)
print(x_train.shape)
input_shape = (round(x_test.shape[1]/5), 5)
model = Sequential()
model.add(Conv1D(100, 2, activation='relu', input_shape=input_shape))
model.add(MaxPooling1D(3))
model.add(Conv1D(100, 2, activation='relu'))
model.add(GlobalAveragePooling1D())
model.add(Dropout(0.5))
model.add(Dense(49, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=2, validation_data=(x_test, y_test))
print(model.summary())
I get this error:
Print out of layers
You are dividing by 5 twice. Here you are reshaping your data, which is necessary contrary to what the other answer says:
x_train = x_train.reshape(x_train.shape[0], round(x_train.shape[1]/5), 5)
x_test = x_test.reshape(x_test.shape[0], round(x_test.shape[1]/5), 5)
This already takes care of "dividing the time by 5". But here you are defining the input shape to the model, dividing by 5 again:
input_shape = (round(x_test.shape[1]/5), 5)
Simply use
input_shape = (x_test.shape[1], 5)
instead! Note that because this shape is called after the reshape, it already refers to the correct one, with the time dimension divided by 5.
You are using Conv1D, but trying, by reshaping, represent your data in 2D - that make a problem. Try to skip the part with reshaping, so your input will be a 1 row with 49 values:
x_train, x_test, y_train, y_test = train_test_split(np_train_data, np_train_target, random_state=0)
print(x_train.shape)
input_shape = (x_test.shape[1], 1)
model = Sequential()
model.add(Conv1D(100, 2, activation='relu', input_shape=input_shape))
model.add(MaxPooling1D(3))
model.add(Conv1D(100, 2, activation='relu'))
model.add(GlobalAveragePooling1D())
model.add(Dropout(0.5))
model.add(Dense(49, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=2, validation_data=(x_test, y_test))
I have run this example and I got the following error when I try to save the model.
import tensorflow as tf
import h5py
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=2)
val_loss, val_acc = model.evaluate(x_test, y_test)
print(val_loss, val_acc)
model.save('model.h5')
new_model = tf.keras.models.load_model('model.h5')
I get this error:
Traceback (most recent call last):
File "/home/zneic/PycharmProjects/test/venv/test.py", line 23, in <module>
model.save('model.h5')
File "/home/zneic/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py", line 1359, in save
'Currently `save` requires model to be a graph network. Consider '
NotImplementedError: Currently `save` requires model to be a graph network. Consider using `save_weights`, in order to save the weights of the model.
Your weights don't seem to be saved or loaded back into the session. Can you try saving the graph and the weights separately and loading them separately?
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
model.save_weights("model.h5")
Then you can load them:
def loadModel(jsonStr, weightStr):
json_file = open(jsonStr, 'r')
loaded_nnet = json_file.read()
json_file.close()
serve_model = tf.keras.models.model_from_json(loaded_nnet)
serve_model.load_weights(weightStr)
serve_model.compile(optimizer=tf.train.AdamOptimizer(),
loss='categorical_crossentropy',
metrics=['accuracy'])
return serve_model
model = loadModel('model.json', 'model.h5')
I have the same problem, and I solved it. I don't konw why, but it works. You can modify as this:
model = tf.keras.Sequential([
layers.Flatten(input_shape=(28, 28)),
layers.Dense(512, activation=tf.nn.relu, input_shape=(784,)),
layers.Dropout(0.2),
layers.Dense(10, activation=tf.nn.softmax)
])
I am trying to convert a tensorflow saved model to tensorflowjs format using this converter.
But this gives me the error IOError: SavedModel file does not exist at:
Though my directory has the Saved Model. It has:
.data-****-of-****,
.meta and .index files.
Am I missing anything?
I had an issue when I tried tensorflowjs_converter, I'd like to use tfjs.converters.save_keras_model method in your python implementation as follow.
Add just one line and that's it then you can import this model from your tensorflowjs project, like I did.
Example code minist_cnn.py
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import tensorflowjs as tfjs
batch_size = 128
num_classes = 10
epochs = 12
# input image dimensions
img_rows, img_cols = 28, 28
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
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(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
# add this line
tfjs.converters.save_keras_model(model, './SavedModel')
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
I want to classify a dataset using Convulation1D in keras.
DataSet Description:
train dataset size = [340,30] ; no of sample = 340 , sample dimension = 30
test dataset size = [230,30] ; no of sample = 230 , sample dimension = 30
label size = 2
Fist I try by the following code using the information from keras site https://keras.io/layers/convolutional/
batch_size=1
nb_epoch = 10
sizeX=340
sizeY=30
model = Sequential()
model.add(Convolution1D(64, 3, border_mode='same', input_shape=(sizeX,sizeY)))
model.add(Convolution1D(32, 3, border_mode='same'))
model.add(Convolution1D(16, 3, border_mode='same'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print('Train...')
model.fit(X_train_transformed, y_train, batch_size=batch_size, nb_epoch=nb_epoch,
validation_data=(X_test, y_test))
score, acc = model.evaluate(X_test_transformed, y_test, batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)
it gives the following error ,
ValueError: Error when checking model input: expected convolution1d_input_1 to have 3 dimensions, but got array with shape (340, 30)
Then I have transformed the Train and Test data into 3 dimension from 2 dimension by using the following code ,
X_train = np.reshape(X_train_transformed, (X_train_transformed.shape[0], X_train_transformed.shape[1], 1))
X_test = np.reshape(X_test_transformed, (X_test_transformed.shape[0], X_test_transformed.shape[1], 1))
Then I run the modified following code ,
batch_size=1
nb_epoch = 10
sizeX=340
sizeY=30
model = Sequential()
model.add(Convolution1D(64, 3, border_mode='same', input_shape=(sizeX,sizeY)))
model.add(Convolution1D(32, 3, border_mode='same'))
model.add(Convolution1D(16, 3, border_mode='same'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print('Train...')
model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch,
validation_data=(X_test, y_test))
score, acc = model.evaluate(X_test, y_test, batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)
But it shows the error ,
ValueError: Error when checking model input: expected convolution1d_input_1 to have shape (None, 340, 30) but got array with shape (340, 30, 1)
I am unable to find the dimension mismatch error here.
With the release of TF 2.0 and tf.keras, you can fairly easily update your model to work with these new versions. This can be done with the following code:
# import tensorflow 2.0
# keras doesn't need to be imported because it is built into tensorflow
from __future__ import absolute_import, division, print_function, unicode_literals
try:
%tensorflow_version 2.x
except Exception:
pass
import tensorflow as tf
batch_size = 1
nb_epoch = 10
# the model only needs the size of the sample as input, explained further below
size = 30
# reshape as you had before
X_train = np.reshape(X_train_transformed, (X_train_transformed.shape[0],
X_train_transformed.shape[1], 1))
X_test = np.reshape(X_test_transformed, (X_test_transformed.shape[0],
X_test_transformed.shape[1], 1))
# define the sequential model using tf.keras
model = tf.keras.Sequential([
# the 1d convolution layers can be defined as shown with the same
# number of filters and kernel size
# instead of border_mode, the parameter is padding
# the input_shape is (the size of each sample, 1), explained below
tf.keras.layers.Conv1D(64, 3, padding='same', input_shape=(size, 1)),
tf.keras.layers.Conv1D(32, 3, padding='same'),
tf.keras.layers.Conv1D(16, 3, padding='same'),
# Dense and Activation can be combined into one layer
# where the dense layer has 1 neuron and a sigmoid activation
tf.keras.layers.Dense(1, activation='sigmoid')
])
# the model can be compiled, fit, and evaluated in the same way
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print('Train...')
model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch,
validation_data=(X_test, y_test))
score, acc = model.evaluate(X_test, y_test, batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)
The problem you are having comes from the input shape of your model. According to the keras documentation the input shape of the model has to be (batch, step, channels). This means that the first dimension is the number of instances you have. The second dimension is the size of each sample. The third dimension is the number of channels which in your case would only be one. Overall, your input shape would be (340, 30, 1). When you actually define the input shape in the model, you only need to specify the the second and third dimension which means your input shape would be (size, 1). The model already expects the first dimension, the number of instances you have, as input so you do not need to specify that dimension.
Can you try this?
X_train = np.reshape(X_train_transformed, (1, X_train_transformed.shape[0], X_train_transformed.shape[1]))
X_test = np.reshape(X_test_transformed, (1, X_test_transformed.shape[0], X_test_transformed.shape[1]))