Am trying to use predict_classes in keras model, eventhough the input shape seems to be as required, the function throws exception
model = get_model()
flist = [10, 1.0, 0.0, 0.0, 1]
X = np.array(flist)
print(X.shape) # prints (5,)
model.predict_classes(X)
it keeps throwing error
ValueError: Error when checking input: expected dense_1_input to have shape (5,) but got array with shape (1,)
Shape of X must be, (Number_of_samples, input_dim). Use np.expand_dims.
X = np.expand_dims(X,axis=0)
Related
I am struggling for the last hour to understand what i am doing wrong. I am a novice in NN, but this is not my first code.
def simple_model(lr=0.1):
X = Input(shape=(6144,))
out = Dense(1)(X)
model = Model(inputs=X, outputs=out)
opt = tf.keras.optimizers.SGD(learning_rate=lr)
model.compile(optimizer=opt, loss='mean_squared_error')
model.summary()
return model
mod = simple_model()
a = np.zeros(6144)
v = mod.predict(a)
running this i get the following error:
WARNING:tensorflow:Model was constructed with shape (None, 6144) for input Tensor("input_1:0", shape=(None, 6144), dtype=float32), but it was called on an input with incompatible shape (32, 1).
......
ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 6144 but received input with shape [32, 1]
Where does this [32, 1] come from ?!
I am sure there is some silly mistake in my code, but can't see it :(
p.s. It does compile the mode and prints the summary before throwing an error
mod = simple_model()
a = np.zeros(6144)
#Add this line
a = np.expand_dims(a,axis=0)
v = mod.predict(a)
The reason why your error appears is that Keras + TensorFlow only allow batch predictions. When we use expand_dims function, we actually create a batch of dimension 1.
I do have two inputs to my network. The one input gets feed through a few linear layers and then should be multiplied elementwise with the other input.
input_a = Input(shape=input_a_shape)
x = Dense(side_channel_speed_output_dimension, activation="relu")(x)
x = tf.reshape(x, [input_shape_image[0], input_shape_image[1]])
x = tf.expand_dims(x, input_shape_image[2])
x = tf.repeat(x, repeats=input_shape_image[2], axis=2)
input_b = Input(shape=input_shape_b)
At this stage I would like to multiply input_a and input_b. How do I do that?
I tried:
input = keras.layers.multiply([input_b, input_a])
There I got this error:
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_2:0", shape=(None, 60, 40, 2), dtype=float32) at layer "input_2". The following previous layers were accessed without issue: ['input_1', 'dense', 'tf_op_layer_Reshape', 'tf_op_layer_ExpandDims', 'tf_op_layer_Repeat/Shape', 'tf_op_layer_Repeat/strided_slice', 'tf_op_layer_Repeat/strided_slice_1', 'tf_op_layer_Repeat/ExpandDims', 'tf_op_layer_Repeat/Tile', 'tf_op_layer_Repeat/concat']
I also tried just tf.multipy(a,b). It does not work either.
Does someone know, how to solve this?
Thanks
I got it now. I need to use this function:
x = keras.layers.multiply([input_image, x])
I'm using Keras Input layer, then I add an embedding layer. Whenever I try to execute the following code
vector = tf.reduce_sum(tf.squared_difference(user, book), 1, name="pos_distances")
ValueError: Input 0 is incompatible with layer prediction: expected
min_ndim=2, found ndim=1
Here is the shape of the input tensors
user : Tensor("Shape:0", shape=(2,), dtype=int32)
book : Tensor("Shape_1:0", shape=(2,), dtype=int32)
It is giving me this error
vector = tf.reduce_sum(tf.squared_difference(user, book), 1, name="pos_distances")
Your tensors don't have the axis=1, over which you are trying to perform the reduction ( second argument of the tf.reduce_sum()). It should rather be
sqrt_diff = tf.squared_difference(user, book)
vector = tf.reduce_sum(sqrt_diff, 0, name="pos_distances")
I'm trying to run this piece of code:
def somefunc(x, rows, n_hidden):
vectors = tf.contrib.layers.embed_sequence(nodes, vocab_size=vocab_size, embed_dim=n_hidden)
batch_size = tf.shape(vectors)[0]
state = tf.zeros([batch_size, rows, n_hidden])
bias = tf.Variable(tf.constant(0.1, shape=[batch_size,1]) # Error here!
...
x = tf.placeholder(tf.int32, shape=[None, 200])
pred = somefunc(x, 200, 40)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=target))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
I get this error when the function is called (error is for bias shape):
TypeError: int() argument must be a string, a bytes-like object or a number, not 'Tensor'
I tried doing b = tf.Variable(0.1, validate_shape=False), but then I got this error at optimizer:
ValueError: as_list() is not defined on an unknown TensorShape.
If I remove validate_shape=False, I get a shape error.
I'm very sorry if I'm overlooking something obvious, but could someone tell me where I'm going wrong?
Thank you very much!
The shape argument of the tf.constant() op expects a static shape, so you can't use a tf.Tensor as part of the argument.
Fortunately there is another op that will suffice: tf.fill(), which allows the shape (its dims argument) to be a tf.Tensor. This means you can define bias as:
bias = tf.Variable(tf.fill(dims=[batch_size, 1], 0.1), validate_shape=False)
I have a variable batch size, so all of my inputs are of the form
tf.placeholder(tf.float32, shape=(None, ...)
to accept the variable batch sizes. However, how might you create a constant value with variable batch size? The issue is with this line:
log_probs = tf.constant(0.0, dtype=tf.float32, shape=[None, 1])
It is giving me an error:
TypeError: unsupported operand type(s) for *: 'NoneType' and 'int'
I'm sure it is possible to initialize a constant tensor with variable batch size, how might I do so?
I've also tried the following:
tf.constant(0.0, dtype=tf.float32, shape=[-1, 1])
I get this error:
ValueError: Too many elements provided. Needed at most -1, but received 1
A tf.constant() has fixed size and value at graph construction time, so it probably isn't the right op for your application.
If you are trying to create a tensor with a dynamic size and the same (constant) value for every element, you can use tf.fill() and tf.shape() to create an appropriately-shaped tensor. For example, to create a tensor t that has the same shape as input and the value 0.5 everywhere:
input = tf.placeholder(tf.float32, shape=(None, ...))
# `tf.shape(input)` takes the dynamic shape of `input`.
t = tf.fill(tf.shape(input), 0.5)
As Yaroslav mentions in his comment, you may also be able to use (NumPy-style) broadcasting to avoid materializing a tensor with dynamic shape. For example, if input has shape (None, 32) and t has shape (1, 32) then computing tf.mul(input, t) will broadcast t on the first dimension to match the shape of input.
Suppose you want to do something using log_probs. For example, you want to do power operation on a tensor v and a constant log_probs. And you want the shape of log_probs to vary with the shape of v.
v = tf.placeholder(tf.float32, shape=(None, 1)
log_probs = tf.constant(0.0, dtype=tf.float32, shape=[None, 1])
result = tf.pow(v, log_probs)
However, you cannot construct the constant log_probs. While, firstly, you can construct tf.constant just with shape =[1] log_prob = tf.constant(0.0, dtype=tf.float32, shape=[None, 1]). Then use tf.map_fn() to do pow operation for each element of v.
v = tf.placeholder(tf.float32, shape=(None, 1)
log_prob = tf.constant(0.0, dtype=tf.float32, shape=[1])
result = tf.map_fn(lambda ele : tf.pow(ele, log_prob), v)