I am trying to train a neural network on Semantic Role Labeling task (text classification task). The dataset consist of sentences on which the neural network has to be trained to predict a class for each word. Apart from using the embedding matrix, I am also using other features (meta_data_features). The number of classes in Y_train are 61. The number 3306 represents the number of sentences in my dataset (size of my dataset). MAX_LEN = 67. The code for the architecture is:
embedding_layer = Embedding(67,
300,
embeddings_initializer=Constant(embedding_matrix),
input_length=MAX_LEN,
trainable=False)
sentence_input = Input(shape=(67,), dtype='int32')
meta_input = Input(shape=(67,), name='meta_input')
embedded_sequences = embedding_layer(sentence_input)
x_1 = (SimpleRNN(256))(embedded_sequences)
x = concatenate([x_1, meta_input], axis=1)
x = Dropout(0.3)(x)
x = Dense(32, activation='relu')(x)
predictions = Dense(61, activation='softmax')(x)
model = Model([sentence_input,meta_input], predictions)
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['sparse_categorical_accuracy'])
print(model.summary())
The snapshot of model summary is:
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 67) 0
__________________________________________________________________________________________________
embedding_1 (Embedding) (None, 67, 300) 1176000 input_1[0][0]
__________________________________________________________________________________________________
simple_rnn_1 (SimpleRNN) (None, 256) 142592 embedding_1[0][0]
__________________________________________________________________________________________________
meta_input (InputLayer) (None, 67) 0
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 323) 0 simple_rnn_1[0][0]
meta_input[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 323) 0 concatenate_1[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 32) 10368 dropout_1[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 61) 2013 dense_1[0][0]
==================================================================================================
Total params: 1,330,973
Trainable params: 154,973
Non-trainable params: 1,176,000
__________________________________________________________________________________________________
The function call is:
simple_RNN_model_trainable.fit([padded_sentences, meta_data_features], padded_verbs,batch_size=32,epochs=1)
X_train constitutes [padded_sentences, meta_data_features] and Y_train is padded_verbs. Their shapes are:
padded_sentences - (3306, 67)
meta_data_features - (3306, 67)
padded_verbs - (3306, 67, 1)
When I try to fit the model, I get the error, "ValueError: Error when checking target: expected dense_2 to have 2 dimensions, but got array with shape (3306, 67, 1)"
It would be great if somebody can help me in resolving the error. Thanks!
Related
I am working on a dataset about student performance in a course, and I want to predict student level (low, mid, high) according to their previous year's marks. I'm using a CNN for this purpose, but when I build and fit the model I get this error:
ValueError: Input 0 of layer sequential_40 is incompatible with the layer: : expected min_ndim=3, found ndim=2. Full shape received: (None, 58)
This is the code:
#reshaping data
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1]))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1]))
#checking the shape after reshaping
print(X_train.shape)
print(X_test.shape)
#normalizing the pixel values
X_train=X_train/255
X_test=X_test/255
#defining model
model=Sequential()
#adding convolution layer
model.add(Conv1D(32,3, activation='relu',input_shape=(28,1)))
#adding pooling layer
model.add(MaxPool1D(pool_size=2))
#adding fully connected layer
model.add(Flatten())
model.add(Dense(100,activation='relu'))
#adding output layer
model.add(Dense(10,activation='softmax'))
#compiling the model
model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
model.summary()
#fitting the model
model.fit(X_train,y_train,epochs=10)
This is the output:
Model: "sequential_40"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1d_23 (Conv1D) (None, 9, 32) 128
_________________________________________________________________
max_pooling1d_19 (MaxPooling (None, 4, 32) 0
_________________________________________________________________
flatten_15 (Flatten) (None, 128) 0
_________________________________________________________________
dense_30 (Dense) (None, 100) 12900
_________________________________________________________________
dense_31 (Dense) (None, 10) 1010
=================================================================
Total params: 14,038
Trainable params: 14,038
Non-trainable params: 0
I'm trying to train my keras model but shapes are incompatible.
The error says
ValueError: Shapes (None, 3) and (None, 3, 3) are incompatible
My train set's shape is (2000, 3, 768) and lable's shape is (2000, 3).
What is the wrong the point?
Model define & fit code
input_shape = x_train.shape[1:]
model = my_dnn(input_shape, 3)
model.fit(x_train, y_train, epochs=25, verbose=1)
Model code
def my_dnn(input, num_classes):
model = Sequential()
model.add(tf.keras.Input(input))
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dense(225))
model.add(Activation('relu'))
model.add(Dense(100))
model.add(Activation('relu'))
model.add(Dense(num_classes))
model.add(Activation('sigmoid'))
model.compile( loss='categorical_crossentropy',
optimizer='adam',
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
return model
In addition to what's said, it seems you are carrying the second dimension of the input data until the end of the model. So your model summary is something like this:
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 3, 1024) 787456
_________________________________________________________________
activation_1 (Activation) (None, 3, 1024) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 3, 1024) 0
_________________________________________________________________
dense_2 (Dense) (None, 3, 512) 524800
_________________________________________________________________
activation_2 (Activation) (None, 3, 512) 0
_________________________________________________________________
dense_3 (Dense) (None, 3, 225) 115425
_________________________________________________________________
activation_3 (Activation) (None, 3, 225) 0
_________________________________________________________________
dense_4 (Dense) (None, 3, 100) 22600
_________________________________________________________________
activation_4 (Activation) (None, 3, 100) 0
_________________________________________________________________
dense_5 (Dense) (None, 3, 3) 303
_________________________________________________________________
activation_5 (Activation) (None, 3, 3) 0
=================================================================
Total params: 1,450,584
Trainable params: 1,450,584
Non-trainable params: 0
As you can see, the output shape of the model (None, 3, 3) is not compatible with the label's shape (None, 3), and at some point, you need to use a Flatten layer.
There are two possible reasons:
Your problem is multi-class classification, hence you need softmax instead of sigmoid + accuracy or CategoricalAccuracy() as a metric.
Your problem is multi-label classification, hence you need binary_crossentropy and tf.keras.metrics.BinaryAccuracy()
Depending on how your dataset is built/the task you are trying to solve, you need to opt for one of those.
For case 1, ensure your data is OHE(one-hot encoded).
Also, Marco Cerliani and Amir (in the comment below) point out that the data output needs to be in a 2D format rather than 3D : you should either preprocess the data accordingly before feeding it to the network or use, as suggested in the comment below, a Flatten() at a point (probably before the final Dense())
I am trying to use the following custom accuracy function in my model:
def acc_fn(pred, gt):
pred_occupy = pred[..., 1] >= config.IOU_THRESHOLD
I1 = tf.reduce_sum(tf.cast(tf.math.logical_and(pred_occupy, tf.cast(gt, tf.bool)), tf.float32))
I2 = tf.reduce_sum(tf.cast(tf.math.logical_or(pred_occupy, tf.cast(gt, tf.bool)), tf.float32))
IoU = tf.math.divide(I1, I2, name = "IoU")
tf.summary.scalar("IoU", IoU)
return IoU
invoked in keras like:
model.compile(loss=loss_fn, #categorical crossentropy
optimizer=keras.optimizers.Adam(learning_rate=config.LR),
metrics=[acc_fn])
I am getting incompatible shapes error while fitting the model:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [1,128,128,128,2] vs. [1,128,128,128]
My model outputs a hot encoded layer of shape [1,128,128,128,2] and the ground truth array is hot-encoded as well and of the same shape!
the last couple of layers in my model
add_10 (Add) (None, 128, 128, 128 0 conv3d_11[0][0]
conv3d_13[0][0]
__________________________________________________________________________________________________
conv3d_14 (Conv3D) (None, 128, 128, 128 433 add_10[0][0]
__________________________________________________________________________________________________
lambda_2 (Lambda) (None, 128, 128, 128 0 conv3d_14[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 128, 128, 128 0 conv3d_14[0][0]
lambda_2[0][0]
__________________________________________________________________________________________________
softmax_1 (Softmax) (None, 128, 128, 128 0 concatenate_1[0][0]
==================================================================================================
Total params: 176,458,081
Trainable params: 176,451,105
Non-trainable params: 6,976
While trying to get a Grad-CAM for my custom model, I ran into a problem. I am trying to fine-tune a model for image classification, using resnet50. My model is defined in the following way:
IMG_SHAPE = (img_height,img_width) + (3,)
base_model = tf.keras.applications.ResNet50(input_shape=IMG_SHAPE, include_top=False, weights='imagenet')
and,
preprocess_input = tf.keras.applications.resnet50.preprocess_input
and finnaly,
input_layer = tf.keras.Input(shape=(img_height, img_width, 3),name="input_layer")
x = preprocess_input(input_layer)
x = base_model(x, training=False)
x = tf.keras.layers.GlobalAveragePooling2D(name="global_average_layer")(x)
x = tf.keras.layers.Dropout(0.2,name="dropout_layer")(x)
x = tf.keras.layers.Dense(4,name="training_layer")(x)
outputs = tf.keras.layers.Dense(4,name="prediction_layer")(x)
model = tf.keras.Model(input_layer, outputs)
Now, I was following the tutorial at https://keras.io/examples/vision/grad_cam/ in order to get a heatmap. But, while the tutorial recommends using model.summary() to get the last convolutional layer and classifier layers, I am not sure how to do it for my model.
If I run model.summary(), i get:
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_layer (InputLayer) [(None, 224, 224, 3)] 0
__________________________________________________________________________________________________
tf.operators.getitem_11 (None, 224, 224, 3) 0
__________________________________________________________________________________________________
tf.nn.bias_add_11 (TFOpLambd [(None, 224, 224, 3)] 0
__________________________________________________________________________________________________
resnet50 (Functional) (None, 7, 7, 2048) 23587712
__________________________________________________________________________________________________
global_average (GlobalAverag (None, 2048) 0
__________________________________________________________________________________________________
dropout_layer (Dropout) (None, 2048) 0
__________________________________________________________________________________________________
hidden_layer (Dense) (None, 4) 8196
__________________________________________________________________________________________________
predict_layer (Dense) (None, 4) 20
==================================================================================================
However, if I run base_model.summary(), i get:
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_29 (InputLayer) [(None, 224, 224, 3) 0
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D) (None, 230, 230, 3) 0 input_29[0][0]
__________________________________________________________________________________________________
conv1_conv (Conv2D) (None, 112, 112, 64) 9472 conv1_pad[0][0]
__________________________________________________________________________________________________
conv1_bn (BatchNormalization) (None, 112, 112, 64) 256 conv1_conv[0][0]
__________________________________________________________________________________________________
... ... ... ...
__________________________________________________________________________________________________
conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048) 8192 conv5_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_add (Add) (None, 7, 7, 2048) 0 conv5_block2_out[0][0]
conv5_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_out (Activation) (None, 7, 7, 2048) 0 conv5_block3_add[0][0]
==================================================================================================
If i follow the tutorial using 'resnet50' as the last convolutional layer, i get the following error:
Graph disconnected: cannot obtain value for tensor KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_29'), name='input_29', description="created by layer 'input_29'") at layer "conv1_pad". The following previous layers were accessed without issue: []
but if I use 'conv5_block3_out', the program cannot find that layer on the model. How can I acess the layers that seem to be hidden on the resnet50 layer?
I managed to find a solution to this problem. When defining "make-gradcam_heatmap", I added the line
input_layer = model.get_layer('resnet50').get_layer('input_1').input
and changed the next line to
last_conv_layer = model.get_layer(last_conv_layer_name).get_layer("conv5_block3_out")
i have a Simple RNN model with below code:
s_input = Input((window_size, ), dtype='int32', name='S')
t_input = Input((window_size, ), dtype='int32', name='T')
emb1 = Embedding(nb_points + 1, emb_size1)
emb2 = Embedding(tm_length + 1, emb_size2)
xe = emb1(s_input)
he = emb2(t_input)
x = Concatenate()([xe, he])
x = SimpleRNN(rnn_size)(x)
y = Dense(nb_points, activation='softmax')(x)
model = Model([s_input, t_input], y)
model.compile('adadelta', 'categorical_crossentropy', metrics=['accuracy'])
return model
When i try to use and called the model. I have this model summary:
Layer (type) Output Shape Param # Connected to
==================================================================================================
S (InputLayer) (None, 2) 0
__________________________________________________________________________________________________
T (InputLayer) (None, 2) 0
__________________________________________________________________________________________________
embedding_32 (Embedding) (None, 2, 100) 500 S[0][0]
__________________________________________________________________________________________________
embedding_33 (Embedding) (None, 2, 6) 150 T[0][0]
__________________________________________________________________________________________________
concatenate_15 (Concatenate) (None, 2, 106) 0 embedding_32[0][0]
embedding_33[0][0]
__________________________________________________________________________________________________
simple_rnn_10 (SimpleRNN) (None, 20) 2540 concatenate_15[0][0]
__________________________________________________________________________________________________
dense_4 (Dense) (None, 4) 84 simple_rnn_10[0][0]
==================================================================================================
Total params: 3,274
Trainable params: 3,274
Non-trainable params: 0
_________________________________________________________________________________________________
But, it does not give any accuracy and lost result for each epoch. only print something like this:
Train on 40 samples, validate on 11 samples
Epoch 1/100
Processing user 1.
Is there anyone can help me with this? result for epoch is not printed.