#I am trying to fit neural network model using keras. my data is as follows -
X_train.shape (4372, 47)
y_train.shape (4372,)
X_test.shape (1874, 47)
y_test.shape (1874,)
define the keras model
model = Sequential()
model.add(Dense(12, input_dim=2, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
compile the keras model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
fit the keras model on the dataset
model.fit(X, y, epochs=150,)
I am getting following error message:
ValueError: in user code:
ValueError: Input 0 of layer "sequential_12" is incompatible with the layer: expected shape=(None, 2), found shape=(None, 47)
I know i need to do perform reshape step, but i dont know how. This is my first neural network model. Could anyone explain how to add reshape step? or if there is some other solution...
Related
i am using fashion_mnist images database (60,000 small square 28×28 pixel grayscale images) and i am trying to apply CNN-LSTM in cascading, this is the code i am using:
from tensorflow.keras.datasets import fashion_mnist
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
print("Shape of x_train: {}".format(x_train.shape))
print("Shape of y_train: {}".format(y_train.shape))
print()
print("Shape of x_test: {}".format(x_test.shape))
print("Shape of y_test: {}".format(y_test.shape))
# define CNN model
model = Sequential()
model.add(TimeDistributed(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=(60000,28,28))))
model.add(TimeDistributed(Conv2D(64, (3, 3), activation='relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))
model.add(TimeDistributed((Dropout(0.25))))
model.add(TimeDistributed(Flatten()))
## LSTM
model.add(LSTM(200, activation='relu', return_sequences=True))
model.add(Dense(128, activation='relu'))
model.compile(loss='categorical_crossentropy', optimizer='adam',
metrics=['accuracy'])
##fitting model
model.fit(x_train,y_train,epochs=5)
test_loss, test_acc=model.evaluate(x_test,y_test)
print('Loss: {0}-Acc:{1}')
print(test_acc)
i get the error after running the fitting line, can any one help me solving the error.
Define an Input layer with a four-channel output instead of defining the input with Conv2D layer.
Here's my code:
(x_train, y_train), (x_test, y_test) = mnist.load_data()
def create_model():
model = tf.keras.models.Sequential()
model.add(Conv2D(64, (3, 3), input_shape=x_train.shape[1:], activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
return model
model = create_model()
the input data shape is (60000, 28, 28). its the keras mnist dataset.
and here's the error
ValueError: Input 0 of layer conv2d_1 is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [None, 28, 28]
An I have no idea whats wrong with it.
Input shape
4D tensor with shape: (batch, channels, rows, cols) if data_format is "channels_first" or 4D tensor with shape: (batch, rows, cols, channels) if data_format is "channels_last".
The Input shape is expected as (batch,channels,rows,cols) you have given number of images.
create a variable like image_size=(3,28,28)
and
input_shape = image_size
... This might work for you. or try
input_shape = (3,28,28)
I realized my mistake mnist data has a shape: (sample, width, height) and Conv2D layers require a shape (samples, width, height, depth), so the solution would be to add an extra dimension.
x_train = x_train[..., np.newaxis]
x_test = x_test[..., np.newaxis]
I have a code below which implements an architecture (in grid search), to yield appropriate parameters for input, nodes, epochs, batch size and differenced time series input.
The challenge I have is to convert the neural network from just having one LSTM hidden layer, to multiple LSTM hidden layers.
At the moment, I could only run the code with Dense-type hidden layers, without having any errors thrown, otherwise I get dimension errors, tuple errors and so on.
The problem is only persistent in the neural network architecture section.
Original code that works:
def model_fit(train, config):
# unpack config
n_input, n_nodes, n_epochs, n_batch, n_diff = config
# Data
if n_diff > 0:
train = difference(train, n_diff)
# Time series to supervised format
data = series_to_supervised(train, n_in=n_input)
train_x, train_y = data[:, :-1], data[:, -1]
# Reshaping input data into [samples, timesteps, features]
n_features = 1
train_x = train_x.reshape((train_x.shape[0], train_x.shape[1], n_features))
# Define model for (Grid search architecture)
model = Sequential()
model.add(LSTM(n_nodes, activation='relu', input_shape=(n_input, n_features)))
model.add(Dense(n_nodes, activation='relu'))
model.add(Dense(n_nodes, activation='relu'))
model.add(Dense(n_nodes, activation='relu'))
model.add(Dense(1))
# Compile model (Grid search architecture)
model.compile(loss='mse', optimizer='adam')
# fit model
model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0)
return model
Modified LSTM-hidden layer code, that fails to run:
# Define model for (Grid search architecture)
model = Sequential()
model.add(LSTM(n_nodes, activation='relu', input_shape=(n_input, n_features), return_sequences=True))
model.add(LSTM(n_nodes, activation='relu', return_sequences=True))
model.add(LSTM(n_nodes, activation='relu', return_sequences=True))
model.add(LSTM(n_nodes, activation='relu', return_sequences=True))
model.add(TimeDistributed(Dense(1)))
Another variant that also threw an error - ValueError: Error when checking target: expected time_distributed_4 to have 3 dimensions, but got array with shape (34844, 1)
model = Sequential()
model.add(LSTM(n_nodes, activation='relu', input_shape=(n_input, n_features), return_sequences=True))
model.add(LSTM(n_nodes, activation='relu', return_sequences=False))
model.add(RepeatVector(n_input))
model.add(LSTM(n_nodes, activation='relu', return_sequences=True))
model.add(LSTM(n_nodes, activation='relu', return_sequences=True))
model.add(TimeDistributed(Dense(n_features)))
Could anyone with any suggestion please help me ?
Try to set return_sequences=False at the last layer.
Combine two models DropBlock(dimesnions=4), and CuDNNGRU(dimension=3) for MNIST image Classification. How can I combine different dimensions of Drop Block and GRU models?
GRU mostly used for Document classification but here I'm trying to classify MNIST dataset. Both Models separately score ~98%. I want to combine both DropBlock and GRU Model in a single classification Model.
The codes of combining the DropBlock and GRU model for MNIST classification.
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train/255.0
x_test = x_test/255.0
print(x_train.shape)
print(x_train[0].shape)
#load the model
model = Sequential()
gru = Sequential()
gru.add(CuDNNGRU(128,input_shape=(x_train.shape[1:]),
return_sequences=True))
gru.add(BatchNormalization())
gru.add(CuDNNGRU(128))
gru.add(BatchNormalization())
print(gru.output_shape)
drop = Sequential()
drop.add(DropBlock2D(input_shape=(28, 28, 1), block_size=7,
keep_prob=0.8, name='Input-Dropout'))
drop.add(Conv2D(filters=64, kernel_size=3, activation='relu',
padding='same', name='Conv-1'))
drop.add(MaxPool2D(pool_size=2, name='Pool-1'))
drop.add(DropBlock2D(block_size=5, keep_prob=0.8, name='Dropout-1'))
drop.add(Conv2D(filters=32, kernel_size=3, activation='relu',
padding='same', name='Conv-2'))
drop.add(MaxPool2D(pool_size=2, name='Pool-2'))
print(drop.output_shape)
model.add(merge([drop, gru], mode='concat', concat_axis=2))
print(model.output_shape)
model.add(Flatten(name='Flatten'))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
score = model.evaluate(x_test, y_test, verbose=0)
The expected result ~98% results on this classification model.
I'm trying to use maxpooling as a first layer using keras and I have a problem with the input and output dimensions.
print(x_train.shape)
print(y_train.shape)
(15662, 6)
(15662,)
x_train = np.reshape(x_train, (-1,15662, 6))
y_train = label_array.reshape(1, -1)
model = Sequential()
model.add(MaxPooling1D(pool_size = 2 , strides=1, input_shape = (15662,6)))
model.add(Dense(5, activation='relu'))
model.add(Flatten())
model.add(Dense(1, activation='softmax'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=
['accuracy'])
model.fit(x_train, y_train, batch_size= 32, epochs=1)
After running the model, I get the following error:
ValueError: Error when checking target: expected dense_622 (last layer)
to have shape (1,) but got array with shape (15662,)
I'm doing classification and my target is binary (0,1)
Thank you
Your target should have shape (batch_size, 1) but you are passing an array of shape (1, 15662). It seems like 15662 should be the batch size, in which case x_train should have shape (15662, 6) and y_train should have shape (15662, 1). In this case however, it doesn't make any sense to have a MaxPooling1D layer as the first layer of your model since max pooling requires a 3D input (i.e. shape (batch_size, time_steps, features)). You probably want to leave out the max pooling layer (and the Flatten layer). The following code should work:
# x_train: (15662, 6)
# y_train: (15662,)
model = Sequential()
model.add(Dense(5, activation='relu', input_shape=(6,))) # Note: don't specify the batch size in input_shape
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=
['accuracy'])
model.fit(x_train, y_train, batch_size= 32, epochs=1)
But it of course depends on what kind of data you have.