I am working on MNIST dataset, in which X_train = (42000,28,28,1) is the training set. y_train = (42000,10) is the corresponding label set. Now I create an iterator from the image generator using Keras as follows;
iter=datagen.flow(X_train,y_train,batch_size=32)
which works fine.
Then I train the model using;
model.fit_generator(iter,steps_per_epoch=len(X_train)/32,epochs=1)
Here it gives the following error;
ValueError: Error when checking input: expected dense_9_input to have 2 dimensions, but got array with shape (32, 28, 28, 1)
I tried but failed to find the mistake. Also I searched here but there was no answer:
expected dense_218_input to have 2 dimensions, but got array with shape (512, 28, 28, 1)
BTW this is the summary of my model
Please help me.
Update:
model=Sequential()
model.add(Dense(256,activation='relu',kernel_initializer='he_normal',input_shape=(28,28,1)))
model.add(Flatten())
model.add(Dense(10,activation='softmax',kernel_initializer='he_normal'))
Shape mismatch was the root-cause. Input shape was not matching with what ImageDataGenetor expects. Please check the following example with mnist data. I have used Tensorflow 2.1.
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
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
x_train = tf.expand_dims(x_train,axis=-1)
x_test = tf.expand_dims(x_test,axis=-1)
datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2)
iter=datagen.flow(x_train,y_train,batch_size=32)
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28,1)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
#model.fit_generator(iter,steps_per_epoch=len(X_train)/32,epochs=1) # deprecated in TF2.1
model.fit_generator(iter,steps_per_epoch=len(iter),epochs=1)
model.evaluate(x_test, y_test)
Full code is here
Related
I want to try to test some hyperparameters, thats i want to use the GridSearchCV, because it seems like thats the way to do it.
But i also want to use the validation split. To use Callsbacks like EarlyStopping or/and ReduceLROnPlateau. So my question is:
How do i implement GridSearchCV + validation_split correctly that none of the data in validation split is using for training and the whole training set is used to train my model?
Afaik GridSearchCV split again my remaining train data (which is 1-validation_split) and split it again? I get kinda high accuracy and im thinking that i dont split the data correctly
model = KerasClassifier(build_fn=create_model,verbose=2, validation_split=0.1)
optimizers = ['rmsprop', 'adam']
init = ['glorot_uniform',
#'normal',
'uniform',
'he_normal',
#'lecun_normal',
#'he_uniform'
]
epochs = [3] #5,8,10,30
batches = [64] #32,64
param_grid = dict(optimizer=optimizers, epochs=epochs, batch_size=batches, init=init)
grid = GridSearchCV(estimator=model, param_grid=param_grid)
grid_result = grid.fit(X_train, Y_train)
You can use your self-defined validation data by passing an extra argument to the grid.fit() function that is validation_data=(X_test, Y_test). The documentation, states that grid.fit() function accepts all valid arguments that can be passed to the actual model.fit() function of the default Keras model. Therefore, you can pass the validation data through the grid.fit() function. You may also pass the callback functions there.
I am adding a working code below (applied on MNIST-digit dataset). Notice how I added the validation data on grid.fit() and removed the 'validation_split':
import tensorflow as tf
import numpy as np
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
(X_train, Y_train), (X_test, Y_test) = mnist.load_data()
Y_train = to_categorical(Y_train, 10)
Y_test = to_categorical(Y_test, 10)
X_train = np.expand_dims(X_train, 3)
X_test = np.expand_dims(X_test, 3)
def create_model(optimizer, init):
model = tf.keras.Sequential([
tf.keras.layers.Convolution2D(32, 3, input_shape=(28, 28, 1),
activation='relu', kernel_initializer=init),
tf.keras.layers.Convolution2D(32, 3, activation='relu',
kernel_initializer=init),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(12, activation='relu',
kernel_initializer=init),
tf.keras.layers.Dense(10, activation='softmax',
kernel_initializer=init),
])
model.compile(loss='categorical_crossentropy',
optimizer=optimizer, metrics=['accuracy'])
return model
model = KerasClassifier(build_fn=create_model, verbose=2,)
optimizers = ['rmsprop', 'adam']
init = ['glorot_uniform',
#'normal',
'uniform',
'he_normal',
#'lecun_normal',
#'he_uniform'
]
epochs = [4,]
batches = [32, 64]
param_grid = dict(optimizer=optimizers, nb_epoch=epochs,
batch_size=batches, init=init)
grid = GridSearchCV(estimator=model, param_grid=param_grid)
grid_result = grid.fit(X_train, Y_train, validation_data=(X_test, Y_test))
Hope this helps. Thanks.
I want to train MobileNetV2 from scratch on CIFAR-100 and I get the following results where it just stops learning after some while.
Here is my code. I would like to see at least 60-70% validation accuracy and I wonder whether I have to pre-train it on imagenet or whether it is because CIFAR100 is just 32x32x3?
Due to some restrictions, I am using Keras 2.2.4 with tensorflow 1.12.0.
from keras.applications.mobilenet_v2 import MobileNetV2
[..]
(x_train, y_train), (x_test, y_test) = cifar100.load_data()
x_train = x_train / 255
x_test = x_test / 255
y_train = np_utils.to_categorical(y_train, 100)
y_test = np_utils.to_categorical(y_test, 100)
input_tensor = Input(shape=(32,32,3))
x = MobileNetV2(include_top=False,
weights=None,
classes=100)(input_tensor)
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)
preds = Dense(100, activation='softmax')(x)
model = Model(inputs=[input_tensor], outputs=[preds])
optimizer = Adam(lr=1e-3)
model.compile(loss="categorical_crossentropy",
optimizer=optimizer,
metrics=['accuracy'])
epochs = 300
batch_size = 64
callbacks = [ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), cooldown=0, patience=10, min_lr=1e-6)]
generator = ImageDataGenerator(rotation_range=15,
width_shift_range=5. / 32,
height_shift_range=5. / 32,
horizontal_flip=True)
generator.fit(x_train)
model.fit_generator(generator.flow(x_train, y_train),
validation_data=(x_test, y_test),
steps_per_epoch=(len(x_train) // batch_size),
epochs=epochs, verbose=1,
callbacks=callbacks)
Well, MobileNets and all other imagenet based models down-sampling the image for 5 times(224 -> 7) and then do GlobalAveragePooling2D and then the output layers.
I think using 32*32 images on these models directly won't give you a good result, as the tensor shape would be 1*1 even before the GlobalAveragePooling2D.
Maybe you should try resize the image to like 96*96 or remove the first stride=2. Take the NASNet paper as reference, they use 4 poolings in both Cifar and ImageNet versions while only ImageNet version has stride=2 in the first Convolution layer.
I am learning image classification using transfer learning(vgg16) and I am using inbuilt fashion mnist dataset of keras.
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
to preprocess the data for vgg16, I used the below commands by importing preprocess_input from keras.applications.vgg16
X_train = preprocess_input(x_train)
X_test = preprocess_input(x_test)
train_features = vgg16.predict(np.array(X_train), batch_size=256, verbose=1)
test_features = vgg16.predict(np.array(X_test), batch_size=256, verbose=1)
but I am getting the below error
ValueError: Error when checking input: expected input_1 to have 4 dimensions, but got array with shape (60000, 28, 28)
I am using keras2.2.4, pip 19.0.3
Fashion mnist dataset has grayscale images it means it has only single channel in depth and VGG16 is trained with RGB images with 3 channels in depth. According to your error you can not use VGG16 with single channel input. To use VGG16 for fashion mnist dataset you have to read images as three channel. You can further process your X_train and X_test as follows using np.stack:
import numpy as np
X_train = np.stack((X_train,)*3, axis=-1)
X_test = np.stack((X_test,)*3, axis=-1)
VGG accepts a minimum of 32 and max of 224, which can be seen here, to reshape this, we can do
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1) # converting it to (,28x28x1)
x_train = np.pad(x_train, ((0,0),(2,2),(2,2),(0,0)), 'constant',constant_values=(0, 0)) # converting it to min (,32x32x1)
x_train = np.stack((x_train,)*3, axis=-1) # (,32,32,1,3)
x_train = x_train[:,:,:,0,:] # (,32,32,1)
y_train = keras.utils.to_categorical(y_train, num_classes)
This can be used easily for .fit(), .evaluate() and .predict() in keras without the need to convert it into tensor data and write generators.
I have a simple code, which DOES work, for training a Keras model in Tensorflow using numpy arrays as features and labels. If I then wrap these numpy arrays using tf.data.Dataset.from_tensor_slices in order to train the same Keras model using a tensorflow dataset, I get an error. I haven't been able to figure out why (it may be a tensorflow or keras bug, but I may also be missing something). I'm on python 3, tensorflow is 1.10.0, numpy is 1.14.5, no GPU involved.
OBS1: The possibility of using tf.data.Dataset as a Keras input is showed in https://www.tensorflow.org/guide/keras, under "Input tf.data datasets".
OBS2: In the code below, the code under "#Train with numpy arrays" is being executed, using numpy arrays. If this code is commented and the code under "#Train with tf.data datasets" is used instead, the error will be reproduced.
OBS3: In line 13, which is commented and starts with "###WORKAROUND 1###", if the comment is removed and the line is used for tf.data.Dataset inputs, the error changes, even though I can't completely understand why.
The complete code is:
import tensorflow as tf
import numpy as np
np.random.seed(1)
tf.set_random_seed(1)
print(tf.__version__)
print(np.__version__)
#Import mnist dataset as numpy arrays
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()#Import
x_train, x_test = x_train / 255.0, x_test / 255.0 #normalizing
###WORKAROUND 1###y_train, y_test = (y_train.astype(dtype='float32'), y_test.astype(dtype='float32'))
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1]*x_train.shape[2])) #reshaping 28 x 28 images to 1D vectors, similar to Flatten layer in Keras
batch_size = 32
#Create a tf.data.Dataset object equivalent to this data
tfdata_dataset_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
tfdata_dataset_train = tfdata_dataset_train.batch(batch_size).repeat()
#Creates model
keras_model = tf.keras.models.Sequential([
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2, seed=1),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
#Compile the model
keras_model.compile(optimizer='adam',
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=['accuracy'])
#Train with numpy arrays
keras_training_history = keras_model.fit(x_train,
y_train,
initial_epoch=0,
epochs=1,
batch_size=batch_size
)
#Train with tf.data datasets
#keras_training_history = keras_model.fit(tfdata_dataset_train,
# initial_epoch=0,
# epochs=1,
# steps_per_epoch=60000//batch_size
# )
print(keras_training_history.history)
The error observed when using tf.data.Dataset as input is:
(...)
ValueError: Tensor conversion requested dtype uint8 for Tensor with dtype float32: 'Tensor("metrics/acc/Cast:0", shape=(?,), dtype=float32)'
During handling of the above exception, another exception occurred:
(...)
TypeError: Input 'y' of 'Equal' Op has type float32 that does not match type uint8 of argument 'x'.
The error when removing the comment from line 13, as commented above in OBS3, is:
(...)
tensorflow.python.framework.errors_impl.InvalidArgumentError: In[0] is not a matrix
[[Node: dense/MatMul = MatMul[T=DT_FLOAT, _class=["loc:#training/Adam/gradients/dense/MatMul_grad/MatMul_1"], transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_sequential_input_0_0, dense/MatMul/ReadVariableOp)]]
Any help would be appreciated, including comments that you were able to reproduce the errors, so I can report the bug if it is the case.
I just upgraded to Tensorflow 1.10 to execute this code. I think that is the answer which is also discussed in the other Stackoverflow thread
This code executes but only if I remove the normalization as that line seems to use too much CPU memory. I see messages indicating that. I also reduced the cores.
import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, Input
np.random.seed(1)
tf.set_random_seed(1)
batch_size = 128
NUM_CLASSES = 10
print(tf.__version__)
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
#x_train, x_test = x_train / 255.0, x_test / 255.0 #normalizing
def tfdata_generator(images, labels, is_training, batch_size=128):
'''Construct a data generator using tf.Dataset'''
def preprocess_fn(image, label):
'''A transformation function to preprocess raw data
into trainable input. '''
x = tf.reshape(tf.cast(image, tf.float32), (28, 28, 1))
y = tf.one_hot(tf.cast(label, tf.uint8), NUM_CLASSES)
return x, y
dataset = tf.data.Dataset.from_tensor_slices((images, labels))
if is_training:
dataset = dataset.shuffle(1000) # depends on sample size
# Transform and batch data at the same time
dataset = dataset.apply(tf.contrib.data.map_and_batch(
preprocess_fn, batch_size,
num_parallel_batches=2, # cpu cores
drop_remainder=True if is_training else False))
dataset = dataset.repeat()
dataset = dataset.prefetch(tf.contrib.data.AUTOTUNE)
return dataset
training_set = tfdata_generator(x_train, y_train,is_training=True, batch_size=batch_size)
testing_set = tfdata_generator(x_test, y_test, is_training=False, batch_size=batch_size)
inputs = Input(shape=(28, 28, 1))
x = Conv2D(32, (3, 3), activation='relu', padding='valid')(inputs)
x = MaxPool2D(pool_size=(2, 2))(x)
x = Conv2D(64, (3, 3), activation='relu')(x)
x = MaxPool2D(pool_size=(2, 2))(x)
x = Flatten()(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
outputs = Dense(NUM_CLASSES, activation='softmax')(x)
keras_model = tf.keras.Model(inputs, outputs)
#Compile the model
keras_model.compile('adam', 'categorical_crossentropy', metrics=['acc'])
#Train with tf.data datasets
keras_training_history = keras_model.fit(
training_set.make_one_shot_iterator(),
steps_per_epoch=len(x_train) // batch_size,
epochs=5,
validation_data=testing_set.make_one_shot_iterator(),
validation_steps=len(x_test) // batch_size,
verbose=1)
print(keras_training_history.history)
Installing the tf-nightly build, together with changing dtypes of some tensors (the error changes after installing tf-nightly), solved the problem, so it is an issue which (hopefully) will be solved in 1.11.
Related material: https://github.com/tensorflow/tensorflow/issues/21894
I am wondering how Keras is able to do 5 epochs when the
make_one_shot_iterator() which only supports iterating once through a
dataset?
could be given smth like iterations = len(y_train) * epochs - here shown for tf.v1
the code from Mohan Radhakrishnan still works in tf.v2 with little corrections in objects' belongings to new classes (in tf.v2) fixings - to make the code up-to-date... No more make_one_shot_iterator() needed
# >> author: Mohan Radhakrishnan
import tensorflow as tf
import tensorflow.keras
import numpy as np
from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, Input
np.random.seed(1)
tf.random.set_seed(1)
batch_size = 128
NUM_CLASSES = 10
print(tf.__version__)
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
#x_train, x_test = x_train / 255.0, x_test / 255.0 #normalizing
def tfdata_generator(images, labels, is_training, batch_size=128):
'''Construct a data generator using tf.Dataset'''
def preprocess_fn(image, label):
'''A transformation function to preprocess raw data
into trainable input. '''
x = tf.reshape(tf.cast(image, tf.float32), (28, 28, 1))
y = tf.one_hot(tf.cast(label, tf.uint8), NUM_CLASSES)
return x, y
dataset = tf.data.Dataset.from_tensor_slices((images, labels))
if is_training:
dataset = dataset.shuffle(1000) # depends on sample size
# Transform and batch data at the same time
dataset = dataset.apply( tf.data.experimental.map_and_batch(
preprocess_fn, batch_size,
num_parallel_batches=2, # cpu cores
drop_remainder=True if is_training else False))
dataset = dataset.repeat()
dataset = dataset.prefetch( tf.data.experimental.AUTOTUNE)
return dataset
training_set = tfdata_generator(x_train, y_train,is_training=True, batch_size=batch_size)
testing_set = tfdata_generator(x_test, y_test, is_training=False, batch_size=batch_size)
inputs = Input(shape=(28, 28, 1))
x = Conv2D(32, (3, 3), activation='relu', padding='valid')(inputs)
x = MaxPool2D(pool_size=(2, 2))(x)
x = Conv2D(64, (3, 3), activation='relu')(x)
x = MaxPool2D(pool_size=(2, 2))(x)
x = Flatten()(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
outputs = Dense(NUM_CLASSES, activation='softmax')(x)
keras_model = tf.keras.Model(inputs, outputs)
#Compile the model
keras_model.compile('adam', 'categorical_crossentropy', metrics=['acc'])
#Train with tf.data datasets
# training_set.make_one_shot_iterator() - 'PrefetchDataset' object has no attribute 'make_one_shot_iterator'
keras_training_history = keras_model.fit(
training_set,
steps_per_epoch=len(x_train) // batch_size,
epochs=5,
validation_data=testing_set,
validation_steps=len(x_test) // batch_size,
verbose=1)
print(keras_training_history.history)
not loading data locally, just easy DataFlow - that is very convinient - Thanks a lot - hope my corrections are proper
For below code i have save models weights in mnist_weights1234.h5. and want to create same file like mnist_weights1234.h5 with same layer configuration
import keras
from __future__ import print_function
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 numpy as np
from sklearn.model_selection import train_test_split
batch_size = 128
num_classes = 3
epochs = 1
# input image dimensions
img_rows, img_cols = 28, 28
#Just for reducing data set
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x1_train=x_train[y_train==0]; y1_train=y_train[y_train==0]
x1_test=x_test[y_test==0];y1_test=y_test[y_test==0]
x2_train=x_train[y_train==1];y2_train=y_train[y_train==1]
x2_test=x_test[y_test==1];y2_test=y_test[y_test==1]
x3_train=x_train[y_train==2];y3_train=y_train[y_train==2]
x3_test=x_test[y_test==2];y3_test=y_test[y_test==2]
X=np.concatenate((x1_train,x2_train,x3_train,x1_test,x2_test,x3_test),axis=0)
Y=np.concatenate((y1_train,y2_train,y3_train,y1_test,y2_test,y3_test),axis=0)
# the data, shuffled and split between train and test sets
x_train, x_test, y_train, y_test = train_test_split(X,Y)
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
# 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(1, kernel_size=(2, 2),
activation='relu',
input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(16,16)))
model.add(Flatten())
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.save_weights('mnist_weights1234.h5')
Now i want to create file like mnist_weights.h5. So i use below code and getting error.
hf = h5py.File('mnist_weights12356.h5', 'w')
hf.create_dataset('conv2d_2/conv2d_2/bias', data=weights[0])
hf.create_dataset('conv2d_2/conv2d_2/kernel', data=weights[1])
hf.create_dataset('dense_2/dense_2/bias', data=weights[2])
hf.create_dataset('dense_2/dense_2/kernel', data=weights[3])
hf.create_dataset('flatten_2', data=None)
hf.create_dataset('max_pooling_2d_2', data=None)
hf.close()
But getting following error:TypeError: One of data, shape or dtype must be specified.
How to solve problem
If you want to use weights that are in numpy arrays, simply set the weights in the layers:
model.get_layer('conv2d_2').set_weights([weights[1],weights[0]])
model.get_layer('dense_2').set_weights([weights[3],weights[2]])
If your arrays are stored in files:
array = numpy.load('arrayfile.npy')
You can save the entire model weights as numpy arrays:
numpy.save('weights.npy', model.get_weights())
model.set_weights(numpy.load('weights.npy'))
The error message has your solution. In these lines:
hf.create_dataset('flatten_2', data=None)
hf.create_dataset('max_pooling_2d_2', data=None)
You are giving data equals to None. To create a dataset, the HDF5 library needs a minimum information, and as the error says, you either need to give a dtype (the data type of the dataset' elements), or a non-None data parameter (to infer the shape), or a shape parameter. You are giving none of these, so the error is correct.
Just give enough information in the create_dataset call for a dataset ti be created.